Feature preserving GAN and multi-scale feature enhancement for domain adaption person Re-identification

Feature Preserving GAN and multi-scale Feature Enhancement for Domain Adaption Person Re-identification

Communicated by Dr

Li Sheng

Accepted Manuscript

Feature Preserving GAN and multi-scale Feature Enhancement for Domain Adaption Person Re-identification Xiuping Liu, Hongchen Tan, Xin Tong, Junjie Cao, Jun Zhou PII: DOI: Reference:

S0925-2312(19)31068-9 https://doi.org/10.1016/j.neucom.2019.07.063 NEUCOM 21124

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Neurocomputing

Received date: Revised date: Accepted date:

4 January 2019 18 June 2019 20 July 2019

Please cite this article as: Xiuping Liu, Hongchen Tan, Xin Tong, Junjie Cao, Jun Zhou, Feature Preserving GAN and multi-scale Feature Enhancement for Domain Adaption Person Re-identification, Neurocomputing (2019), doi: https://doi.org/10.1016/j.neucom.2019.07.063

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Feature Preserving GAN and multi-scale Feature Enhancement for Domain Adaption Person Re-identification

a School

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Xiuping Liua , Hongchen Tana,∗, Xin Tongb , Junjie Caoa , Jun Zhoua

of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China Graphics Group, Microsoft Research Asia, Beijing 100080, China

b Internet

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Abstract

The performance of Person Re-identification (Re-ID) model depends much on its training dataset, and drops significantly when the detector is applied to a new scene due to the large variations between the source training dataset and the target scene. In this paper, we proposed multi-scale Feature Enhancement(MFE) Re-ID model and Feature Preserving Generative Adversarial Network (FPGAN)

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for cross-domain person Re-ID task. Here, MFE Re-ID model provides a strong baseline model for cross-domain person Re-ID task, and FPGAN bridges the

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domain gap to improve the performance of Re-ID on target scene. In the MFE Re-ID model, person semantic feaure maps, extracted from backbone of segmentation model, enhance person body region’s multi-scale feature responce.

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This operation could capture multi-scale robust discriminative visual factors related to person. In FPGAN, we translate the labeled images from source to target domain in an unsupervised manner, and learn a transfer function

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to preserve the person perceptual information of source images and ensure the transferred person images show similar styles with the target dataset. Extensive

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experiments demonstrate that combining FPGAN and MFE Re-ID model could achieve state-of-the-art results in cross-domain Re-ID task on DukeMTMC-reID ✩ Fully

documented templates are available in the elsarticle package on CTAN. author Email addresses: [email protected] (Xiuping Liu), [email protected] (Hongchen Tan), [email protected] (Xin Tong), [email protected] (Junjie Cao), [email protected] (Jun Zhou) ∗ Corresponding

Preprint submitted to Journal of LATEX Templates

July 24, 2019

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and Market-1501 datasets. Besides, MFE Re-ID model could achieve state-ofthe-art results in supervised Re-ID task. All source codes and models will be released for comparative study.

GAN, multi-scale Feature Enhancement.

1. Introduction

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Keywords: Domain Adaptation, Person Re-identification, Feature Preserving

Person Re-identification (Re-ID) aims to match person images captured

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from two non-overlapping cameras. Due to the importance in automated video surveillance and forensics, person Re-identification has drawn much attention 5

in the computer vision and machine learning communities [1, 2, 3, 4]. It is also a challenge computer vision task because the visual appearance of a person often undergoes intensive changes in illumination, background, camera viewangle and human pose. The art implicitly addresses this problem by learning

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identity-discriminative but robust visual appearance characteristics or factors. Most existing Re-ID employ deep neural networks (DNNs) to learn robust

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discriminative features such as optimising pairwise matching distance metrics

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[5, 6, 7] or deep learning methods [8, 9, 10]. For matching, the features are typically extracted from the very top feature layer of a trained model. It is

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widely acknowledged [11, 12, 13] that, when progressing from the bottom to the top layers, the visual concepts captured by the feature maps tend to be more abstract. As shown in Figure 1, shallow feature maps contain more de-

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tail information, middle-level feature maps contain more structure contextual information, and deep-level feature maps contain more abstract semantic infor-

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mation. And automatically learning the multi-scale robust discriminative visual

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factors, plays an important role on Re-ID task [10]. In order to capture discriminative factors of multiple levels, [14, 15, 16] fo-

cused on learning semantic visual information with additional person attributes (gender, object carrying, clothing colour/texture, etc). However, annotating attributes is costly and error-prone in some degree. Then, HP-net [17], HA-CNN

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person image

Figure 1:

shallow feature

middle-level feature

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deep-level feature

Shallow feature, middle-level feature and deep-level feature maps of person ex-

tracted from deep convolution model. As we can see that the shallow feature maps contain

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details and texture feature, middle level feature maps contain more structured feature and deep feature maps contain more semantic information.

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[9] and CMDL-Dis [18] automatically learn the attention model to capture local and global feature or multi-scale feature. Different from these outstanding approaches [17], [9] and [18] in attention model, we only pay attention on per-

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son body part, and capture multi-scale discriminative person body visual factors including detail information, structure contextual information, and semantic in30

formation. These operations push the model capture rich robust discriminative

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feature. Based on ID-discriminative Embedding (IDE) [19] Re-ID model, firstly we extract multi-scale feature maps from IDE backbone network to automatically learn the discriminative visual cues that are insensitive to scene condition

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changes. Secondly, we introduce person semantic feature extracted from backbone of segmentation model, to directly enhance the response of person body

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region on these multi-scale feature maps. These operations could reduce the interference of background from different scenes in some degree. As shown in Figure 5, person semantic feature maps producted by backbone of segmentation

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network, have stronger response on person part than other region.

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With the rapid development of image segmentation approaches including

FCN [20], Mask R-CNN [21], DeepLab V2 [22]. It has been proved that the body segmentation is robust to illumination, cloth colors, and this is useful for identifying a person [23]. DeepLab V2 model has good performance on seg-

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mentation task recently. Therefore, we use backbone of DeepLab V2 model 45

as the basic network of our Re-ID model. Based on backbone of DeepLab V2 model, combining multi-scale feature and person semantic feature strategies, we

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proposed a multi-scale Feaure Enhancement (MFE) Re-ID model, as shown in Figure 4. In the MFE Re-ID model, firstly the multi-scale feature maps are extracted from the ”ResNet-101 Original scale” in DeepLab V2’s backbone net50

work. Secondly, person semantic feature map, extracted from backbone network of DeepLab V2 model, enhances the feature response of person part on these

multi-scale feature. Thirdly, this operation products enhanced multi-scale dis-

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criminative feature maps. Because feature from the whole image contains the

global structure feature from scene, finally, discriminative person visual cues 55

are composed of feature of the whole image and above enhanced multi-scale discriminative feature maps. Our MFE Re-ID model could effectively improve the performance of IDE Re-ID model on person Re-ID task.

Although recently many methods [24, 25, 26, 27, 28, 29, 30] have good perfor-

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mance on person Re-ID task, most existing Re-ID studies follow the supervised learning paradigm. The performance of these studies drops significantly in prac-

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tical Re-ID deployments due to the large variations between the source training dataset and the target scene. In general, source dataset and target dataset have different drastically in visual appearance due the different illumination

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conditions, and to the camera configurations and viewing angles. As shown in Figure 2, DukeMTMC [31] images and Market-1501 [32] images have drasti-

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cally different in background and illumination conditions. Besides, this manual annotation task can be very time consuming and expensive when considering the huge number of images from target scene. This significantly limits their

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scalability and usability in real-world large scale deployments with the need for

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performing Re-ID across many scene. A common strategy for this problem is unsupervised domain adaptation,

which is more challenging than normal supervised person Re-ID task. Typically, unsupervised domain adaptation is a way of handling dataset bias [33] and also used to minimize the visual gap between labeled dataset and unlabeled 4

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Duke Images

Market Images

Figure 2: Sample images of (left:) DukeMTMC-reID dataset, (right:) Market-1501 dataset.

real images. While many unsupervised domain adaptation methods have been

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developed [34, 35, 36], they typically offer weaker Re-ID performances when compared to the supervised methods. One main reason is that without labelled data across scenes, unsupervised methods lack the necessary knowledge of target scene due to different view angles, background and illumination. With a 80

varies of novel deep architectures, there are a few of studies pay more atten-

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tion on cross-domain unsupervised person Re-identification, such as unlabeled samples generated by GAN (Generative Adversarial Network)[37], PTGAN [38],

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CamStyle[39], DATS[40] and SPGAN [41]. These approaches based on CycleGAN learn image-image translation models to minimize the gap between source 85

domain and target domain.

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In SPGAN [41], they introduce contrastive loss to CycleGAN [42] to preserve the person ID information, and the distance of two pair images in contrastive

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loss is caculated by feature vector of fully connection layer. As shown in Figure 3, deep-level two-dimensional convolution feature contains rich perceptual

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information including structure semantic information and high frequency fea-

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ture [43, 42], which plays an important role on identifying person and is very limited in fully-connected feature. PTGAN [38] and DATS [40] introduce a person identity loss to CycleGAN [42], which is computed by first acquiring the foreground mask of a raw person image. However, the bad person mask are

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partly resulted by low image resolution or similar foreground and background, which will lead to bad transferred person images. 5

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Convolution Feature Fully Connected Feature

Figure 3:

Fully Connected Layer Feature

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Deep-level Convolution Feature

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……

(upper:)A general convolution network structure, (lower left:) two-dimensional

convolution feature, and (lower right:) we spread a portion of the fully connected layer vector from left to right, from top to bottom. As shown in Figure, two-dimensional convolution feature contains more semantic information and structured context information than fully

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connected layer feature.

In this paper, we aim to extend the CycleGAN [42] framework for image style transfer. We are interested in generating a perceptually high-quality image that

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contain rich person structure semantic information and style information of target scene. To perceptually high-quality images, we introduce perceptual loss terms for the generator in CycleGAN [42], corresponding to feature activation.

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Thus our approach is to regularize the original minimax optimization for CycleGAN [42] with perceptual loss terms. Introducing the perceptual loss into

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GAN, has been applied in many computer vision tasks, including Text-to-Image

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Synthesis [44], Image Super-Resolution [43], and Image Transformation [45]. In this paper, we hope to learn a transfer function to preserve the high-level per-

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ceptual information of source images and ensure the transferred person images show similar styles with the target dataset. Therefore, we proposed Feature Preserving Generative Adversarial Network(FPGAN) by introducing the per-

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ceptual loss [43] to CycleGAN. Perceptual loss enforce preserving perceptual similarity between the real and the generated images.

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In this paper, MFE Re-ID model proposed is designed to improve the performancd of IDE model in supervised Re-ID task, and further to provides the strong baseline Re-ID model for domain adaption Re-ID task. Combining MFE Re-ID model and FPGAN has good performance in domain adaption Re-ID

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task. The contributions of this paper can be summarized as follows:

(1) We propose multi-scale Feature Enhancement(MFE) Re-ID model, effectively improves the performance of IDE model in supervised Re-ID task.

(2) We introduce FPGAN to improve the unsupervised cross-domain person Re-ID by preserving the underlying high-level perceptual information of person during image-image translation.

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(3) In cross-domain and supervised Re-ID task, extensive experiments on two large scale datasets, Market-1501 [32] and DukeMTMC-reID [31], show that our framework could achieve the state-of-the-art results. 2. Related Work

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In this section, we briefly review two-type works that are related to our

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approach: (1) Cross-Domain person Re-ID, (2) Visual Attention Mechanism. 2.1. Cross-Domain person Re-ID Hand-craft features [46, 47, 48, 49, 32] can be directly employed for unsupervised Re-ID in target dataset. But these feature design methods do not fully

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exploit rich information from data distribution. Some methods are based on

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saliency statistics [35, 50]. In [51], K-means clustering is used for learning an unsupervised asymmetric metric. Peng et al. Recently, some transfer learning algorithms [52, 53] are proposed to leverage the Re-ID models pre-trained in source datasets to improve the performance on target dataset. Peng et al.

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[52] propose a multi-task dictionary learning model to transfer a view-invariant representation from a labeled source dataset to an unlabeled target dataset. Geng et al. [53] transfer representations learned from large image classification datasets to Re-ID datasets using a deep neural network which combines classifi-

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cation loss with verification loss. Besides, domain adaption and image-to-image 7

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translation approaches have been applied to Re-ID task increasingly. Deng et al. [41] combine CycleGAN [42] with similarity constraint for domain adaptation which improve performance in cross-dataset setting. Zhong et al. [54]

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introduce camera style transfer approach to address image style variation across multiple views and learn a camera-invariant descriptor subspace. PTGAN [38] and DATS [40] introduce a person identity loss to CycleGAN [42], which is

computed by first acquiring the foreground mask of a raw person image. As described in Section 1, deep-level two-dimensional convolution feature contains

rich perceptual information including structure semantic information and high frequency feature [43, 42], which plays an important role on identifying per-

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son and is very limited in fully-connected feature and pixel-wise image. Thus, different from [41], PTGAN [38] and DATS [40], we introduce the perceptual loss into CycleGAN. And during the transferring process, our FPGAN preserve person perceptual semantic information and capture more style information of target scene.

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2.2. Visual Attention Mechanism

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In many computer vision field, such as person search [55], person Re-ID [56, 57], object tracking [58, 59] and Image Captioning [60, 61], visual attention mechanism been studied. It is efficient and effective via implementing a spatial attention map across each location of the features. In Re-ID task, HP-net [17],

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HA-CNN [9] and CMDL-Dis [18] automatically learn the attention model to capture local and global feature or multi-scale feature. Different from these

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outstanding approaches, we introduce person semantic feature extracted from backbone of segmentation model, to directly enhance the response of person body region on these multi-scale feature maps. And our MFE Re-ID model

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could capture multi-scale discriminative person body visual factors including detail information, structure contextual information, and semantic information. These operations push the model capture rich robust discriminative feature, and further reduce the interference of background from different scenes.

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Figure 4: Multi-scale Feature Enhancement (MFE) Re-ID model in backbone of DeepLab V2 model. The basic network of MFE Re-ID is backbone of DeepLab V2 model (dashed line box). MFE Re-ID model cotains Multi-scale Feature Representation and Person Semantic Feature Enhancement. Details about MFE Re-ID model can be refer to text description in

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subsection 3.1.

3. THE PROPOSED APPROACH

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In this paper, firstly we describe the details of multi-scale Feature Enhancement(MFE) Re-ID model, which provides a strong baseline model for crossdomain adaptation Re-ID. Secondly, based on MFE Re-ID model, we cast per-

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son Re-identification as an unsupervised domain adaptation problem, to find an effectively unsupervised strategy for performing person Re-identification on the

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target scene.

3.1. Multi-scale Feature Enhancement Re-ID model

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Same as ID-discriminative Embedding (IDE) Re-ID model, our MFE Re-

ID model also regards Re-ID training as an image classification task. Firstly,

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we simply describe the IDE Re-ID model. Secondly, we structure multi-scale Feaure Enhancement(MFE) Re-ID model to improve the performance of IDE model in the supervised Re-ID task. Besides, MFE Re-ID model provides the strong baseline model for cross-domain Re-ID task in next subsection. 9

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In the IDE Re-ID model [19], which uses ResNet-50 as backbone and follow 185

the training strategy in [19] for fine-tuning on the ImageNet [62] pre-trained model. Using the Softmax loss, IDE regards Re-ID training as an image clas-

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sification task. Besides, based on the IDE Re-ID model [19], the IDE strong baseline model (S-baseline) [63] introduce some effective training tricks and de-

sign a new neck structure to improve the performance of traditional IDE model 190

in the Re-ID task. For more details about S-baseline, please refer to [63]. In or-

der to improve our proposed method has better performance on supervised and unsupervised Re-ID task, based on more strong IDE baseline model(S-baseline)

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[63], we design our multi-scale Feaure Enhancement(MFE) Re-ID model.

We structure multi-scale Feaure Enhancement(MFE) Re-ID model, as shown 195

in Figure 4. The MFE Re-ID model could effectively improve the performance of IDE model in supervised Re-ID task. In MFE Re-ID model, firstly we use the backbone (three-scale ResNet-101 network, feature fusion part and upsampling part) of DeepLab V2 as our MFE model’s basic network. Secondly, multi-

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scale semantic feature maps are extracted from MFE model’s basic network, to capture multi-scale latent discriminative feature about person. Thirdly, person

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semantic feature is extracted from MFE model’s basic network to enhance the feature response of person part on multi-scale discriminative feature, which could reduce the interference of background. More details about DeepLab V2

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could be found in [22]. Next, we specifically describe the second stage and third stage respectively, and provide the algorithm step of multi-scale feature

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enhancement.

Multi-scale Feature Representation. As shown in bottom part of Fig-

ure 4, we extract the shallow, middle-level and deep-level information of per-

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son from three-scale feature maps of ResNet101-Original Scale branch in MFE

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model. As shown in Figure 1, shallow feature maps contain abundant color or texture feature, middle level feature maps contain more structured feature and deep feature maps contain more semantic information. In this paper, we specifically extract feature map res3d3, res4b22 and res5c in ResNet101-Original Scale branch as three-scale feature maps. By extracting the three-scale feature maps, 10

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our Re-ID model could capture robust multi-level discriminative visual factors to varaint environment. Person Semantic Feature Enhancement.

In addition to capturing

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multi-level discriminative person information, enhancing the feature response of person body region also plays an important role on Re-ID task. As we know 220

that object segmentation is pixel-level classification. Feature map in object seg-

mentation model contains rich semantic feature information. Because DeepLab

V2 has good performance on object segmentation. Therefore, we use back-

bone network (dashed line box in Figure 4) of DeepLab V2 model as MFE

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Re-ID model’s basic network. Backbone network (dashed line box in Figure 4) of DeepLab V2 model contains three scale ResNet-101 network, feature fusion part and upsampling part.

In order to acquire robust multi-scale discriminative person feature, firstly we extracted person semantic feature from MFE Re-ID model’s basic network (backbone network of DeepLab V2 model). As shown in Figure 5, the person semantic feature maps could make the person region more saliency than other

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region, which makes person’s discriminative feature more robust to complex

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scene background. Secondly, applying feature fusion operation on the person semantic feature map with the three-scale Feature maps respectively, and output three scale enhanced feature maps. Thirdly, fusing the these three-scale enhanced feature maps, and then it generates the semantic Enhanced Feature

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map. Besides, feature from the whole image contains the global structure fea-

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ture from scene, which also plays an important role on Re-ID task in some degree. In our MFE Re-ID model, the final discriminative feature are composed of the whole image’s feature and semantic Enhanced Feature.

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Algorithm of multi-scale Feature Enhancement. In this part, we describe the algorithm of multi-scale Feature Enhancement.

The global structure is shown in Figure 4, and visual result of multi-scale Feature Enhancement operation is shown in Figure 5. Step 1: In Figure 4, extract three scale Feature maps (shallow, middle-level

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and deep-level feature maps). Then apply bilinear interpolation on three scale 11

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Shallow Feature

Fusion

Fusion

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+

+

Middle-level Feature

Semantic Enhanced Feature

Deep-level Feature

Person Semantic Feature

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Multi-scale Enhanced Feature Maps

Figure 5: Multi-scale Feature Enhancement. Firstly, extract three scale Feature maps, including Shallow, Middel-level and Deep-level Feature maps. Secondly, extract person semantic feature map. Thirdly, using the person semantic feature map to enhance these three-scale Feature maps, and generates Multi-scale Enhanced Feature Maps. Finally, fusing the Multi-scale enhanced feature maps generates Semantic Enhanced Feature.

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feature maps to adjust the size of these feature maps.

Step 2: Extract person semantic feature map from MFE Re-ID model’s basic network (dashed line box in Figure 4).

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Step 3: Using the person semantic feature map from step 2, to enhances the aboved three scale Feature maps in step 1, and outputs three scale enhanced

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feature maps in Figure 5.

Step 4: Fusing the these three scale enhanced feature maps from step 3, and then it generates Semantic Enhanced Feature map (Semantic En-

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hanced Feature in Figure 5). Apply pooling operation on Semantic Enhanced

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Feature map, and then it generates single vector, namely vector a.

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Step 5: Extract global feature vector, namely vector b in Figure 4. Step 6: Concating vector a and vector b products a L2-normalized feature

vector c.

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In this time, using the final vector c to train the classifier or implement

person Re-ID task.

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Figure 6: Pipeline of the our cross-domain Re-ID framework based on unpaired Image-toImage Translation. First, we translate the labeled images from a source domain to a target

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domain by Feature Preserving GAN (FPGAN). Second, we train our multi-scale Feature

Enhancement (MFE) Re-ID model with the translated images in supervised learning methods.

3.2. Architecture of Domain Adaption person Re-ID

In this section, firstly we describe the pipeline of unsupervised domain adaption person Re-ID task. secondly, more details of our Feature Preserving Gen-

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erative Adversarial Network (FPGAN) are described.

In this paper, we propose multi-scale Feature Enhancement(MFE) Re-ID

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network and Feature Preserving Generative Adversarial Network(FPGAN). MFE Re-ID model could provide strong baseline model for cross-domain Re-ID task. FPGAN performs image-to-image translation and creates a dataset on the tar-

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get domain in an unsupervised manner. The dataset inherits the labels from the source domain and thus can be used in supervised learning in the target

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domain. The overview of the proposed method is shown in Figure 6. We formulate cross-domain Re-ID task as the following two-steps: Firstly, labeled

images from the source domain are transferred to the target domain based on

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FPGAN, so that the transferred image has a similar style with the target. Sec-

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ondly, the style-transferred images and their associated labels are used to train MFE Re-ID model in supervised learning.

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DT

Ds G

Target Dataset Image T

F

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Source Dataset Image S

Figure 7: CycleGAN consists of two mapping functions: G: S −→T and F : T −→S , and

associated adversarial discriminators DT and DS . DT encourages G to translate S into

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outputs indistinguishable from domain T , and vice versa for D S and F .

3.3. Feature Preserving GAN: Approach Details

In this section, we will show more details about FPGAN which translates the annotated dataset S from the source domain to target domain T in an 280

unsupervised manner. Applying this network, we can create a labeled training

3.3.1. CycleGAN Revisit

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dataset G ( S ) on the target domain.

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As shown in Figure 7, CycleGAN introduces two generator-discriminator pairs, {G, DT } and {F, DS }. In the CycleGAN, the generator G maps a sample from source domain S to target domain T and the generator F maps a sample

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from target domain T to source domain S . In addition, CycleGAN include two adversarial discriminators D T and D S . For generator G, and its associated

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discriminator D T , the adversarial loss is

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LTadv (G, DT , px , py ) = IEy∼py [(DT (y) − 1)2 ] + IEx∼px [DT (G(x))2 ]

(1)

where px and py denote the image distributions in the source and target

dataset, respectively. For generator F , and its associated discriminator D S ,

the adversarial loss is LSadv (F, DS , px , py ) = IEx∼px [(DS (x) − 1)2 ] + IEy∼py [DS (F (y))2 ]

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(2)

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Target Dataset Image IT

True Discriminator Network DT

Generator Network G

Target Dataset Style Transfer Image G(Is)

Source Dataset Image Is

Fake

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Source Dataset Image Is

…

…

Φ( G( Is ))

…

…

Φ ( Is )

Network-Φ(·)

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Figure 8: G Branch of the FPGAN, in which SiaNet(Network-Φ(·)) preserves the person

peceptual information during the style transfer, namely translated image and its counter part in the source dataset have the same peceptual feature.

Both cycle consistency losses can be expressed as

(3)

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Lcyc (G, F ) = IEx∼px [kF (G(x)) − 1k1 ] + IEy∼py [kG(F (y)) − yk1 ] Lide loss function could be expressed as

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Lide (G, F ) = IEx∼px [kF (x) − 1k1 ] + IEy∼py [kG(y) − yk1 ]

(4)

As mentioned in [42], Lide loss function could preserve the color composition 285

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under image style translation between source dataset and target dataset. For more details about CycleGAN, please refer to [42].

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3.3.2. FPGAN

Applied in person Re-ID, preserving person feature and capturing image

styles of target scene are essential functions to generate improved samples for

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cross-domain person Re-ID task [41, 40, 38, 39]. In SPGAN [41], they introduce

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contrastive loss to preserve the person ID information, and the distance of two pair images in contrastive loss is caculated by feature vector of fully connection layer. However, as shown in Figure 3, deep-level two-dimensional convolution feature map contains rich perceptual information including structure semantic

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information and high frequency feature [43, 42], which is very limited in fully295

connected feature. PTGAN [38] and DATS [40] introduce a person identity loss to CycleGAN, which is computed by first acquiring the foreground mask of a

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raw person image. However, the bad person mask are partly resulted by low image resolution or similar foreground and background, which will lead to bad transferred person images.

Thus, we hope to learn a transfer function to preserve the high-level per-

ceptual information (person body part feature) of source images and ensure the transferred person images show similar styles with the target dataset. In order

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to achieve the goal, inspired by [44, 43, 45], we proposed Feature Preserving Generative Adversarial Network(FPGAN) by introducing the perceptual loss [43] to CycleGAN. As shown in Figure 8, we show the G branch of FPGAN.

In the G branch of FPGAN, the SiaNet consists of two Network-Φ(·). We extract two-dimensional convolution feature maps as person perceptual feature from Network-Φ(·). And the distance in perceptual loss is calculated by two-

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dimensional convolution feature maps. We design a Feature preserving loss to train SiaNet based on Perceptual loss [43]:

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LP er (x, y) = kΦ(G(x)) − Φ(x)k2F + kΦ(F (y)) − Φ(y)k2F

(5)

where x belongs to source dataset and y belongs to target dataset, Φ(x) indi-

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cates person perceptual feature (deep-level two-dimensional convolution feature) of person image x. Perceptual loss enforce perceptual similarity between the real

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and the generated images.

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Overall objective function in FPGAN can be written as

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LF P = LTadv + LSadv + λ1 Lcyc + λ2 Lide + λ3 LP er

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where λt , t∈ { 1, 3, 5 } controls the relative importance of four objectives.

The first three losses belong to the CycleGAN formulation [42], and the perceptual loss induced by SiaNet imposes a new constraint on the system. In the training phase, FPGAN is divided into three components including the generators, discriminators and a SiaNet. These three components are learned 16

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alternately. When the parameters of the generators and discriminators are fixed, 310

the parameters of the SiaNet is updated. We train the FPGAN until the con-

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vergence or the maximum iterations.

4. Experiment 4.1. Datasets

We select two large-scale Re-ID datasets for experiment, i.e., Market-1501 315

[32] and DukeMTMC-reID [31]. Market-1501 has 12,936 training and 19,732

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testing images with 1,501 identities in total from 6 cameras. We follow the

standard training and evaluation protocols in [26] where 751 identities are used for training and the remaining 750 for testing in a single query setting. DukeMTMC-reID is also a large-scale Re-ID dataset from 8 cameras. There 320

are 16,522 training images of 702 identities, 2,228 query images and 17,661 gallery images of the other 702 identities. Sample images of the two datasets

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are shown in Figure 2. We use Rank-1 accuracy and mean Average Precision (mAP) for evaluation on these two big Re-ID datasets. In the unsu-

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pervised cross-domain adaption experiments, there are two source-target settings. 1. DukeMTMC-reID→Market-1501: Duke images which are translated to Market style, Target Domain is Market-1501 and Source Domain is

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DukeMTMC-reID; 2. Market-1501→DukeMTMC-reID: Market images translated to Duke style, Target Domain is DukeMTMC-reID and Source Do-

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main is Market-1501. 330

4.2. Implementation Details MFE Re-ID model training and testing. Firstly, the DeepLab V2

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model is pretrained on PASCAL VOC 2012 dataset to implement person segmentation task. Secondly, based on backbone network(three scale ResNet-101 network, feature fusion part and upsampling part) of DeepLab V2 model, MFE

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Re-ID model is designed. We use mini-batch SGD to train CNN models on GTX 1080 Ti GPU. Training parameters such as batch size, maximum number

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Table 1: Results of the ablation study in supervised Re-ID task.

Method

Base Network

IDE [19]

DukeMTMC-reID Market-1501 mAP

Rank-1 mAP

ResNet-50

66.7%

46.3%

75.6% 51.9%

IDE+ (S-baseline) [63]

ResNet-50

86.9%

76.3%

94.1% 85.2%

IDE*

ResNet-101

87.4%

77.1%

94.4% 86.0%

IDE*+MS

ResNet-101

88.3%

77.7%

94.9% 86.7%

IDE*+PF

ResNet-101

88.0%

77.5%

95.2% 87.0%

DeepLabV2’s Backbone 88.7%

77.9%

95.8% 87.6%

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Rank-1

epochs, momentum and gamma are set to 16, 50, 0.9 and 0.1, respectively. The initial learning rate is set to 0.001 and then divided by 10 at step 20k, 40k, 60k and 80k. At inference stage, we rank the gallery people according to their 340

feature distances to the target person.

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FPGAN training and testing. We train FPGAN using the training datasets of Market-1501 and DukeMTMC-reID on Tensorflow. Note that, training FPGAN procedure belongs to unsupervised learning due to not using use

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any person ID label. In experiments, empirically set λ1 = 9, λ2 = 4 and λ3 = 2 in Eq. 6. With an initial learning rate 0.0002, and model stop training after 8

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epochs. During the testing procedure, we employ the Generator G for Market1501−→DukeMTMC-reID translation and the Generative F for DukeMTMCreID−→Market-1501 translation. The translated images are only used to train

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the MFE Re-ID model. FPGAN is composed of an Siamese network (SiaNet)

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and a CycleGAN. For CycleGAN, we adopt the architecture described in [42]. For SiaNet, it contains 3 convolutional layers and 3 max pooling layers, config-

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ured as below. (1) Conv. 4 × 4, stride = 2; (2) Max pooling 2 × 2, stride = 2; (3) Conv. 4 × 4, stride = 2; (4) Max pooling 2 × 2, stride = 2; (5) Conv. 4 × 4, stride = 2; (6) Max pool 2 × 2, stride = 2.

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4.3. Ablation Study in MFE Re-ID model In this subsection, we mainly discuss the effectiveness of each strategy in MFE Re-ID model. Firstly, based on the traditional IDE-baseline [19], we in-

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troduce recently more strong baseline [63] as our MFE’s basic model. Secondly, we demonstrate the effectiveness of each strategy by a series of ablation study.

IDE model for Re-ID task. In this part, firstly we introduce more

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strong baseline [63] as our MFE’s basic model. Based on the traditional IDEbaseline (backbone network is ResNet-50) [19], the IDE strong baseline model

(S-baseline) [63] introduce some effective training tricks and design a new neck

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structure to improve the performance of traditional IDE model in the Re-ID task. For more details about S-baseline, please refer to [63]. Table 1 shows that IDE+ (S-baseline) could effectively improve the performance of traditional

IDE model in Re-ID task. As shown in Table 1, IDE+ could achieve 86.9% and 76.3% in Rank-1 accuracy and mAP on DukeMTMC-reID respectively. And it could achieve 94.1% and 85.2% in Rank-1 accuracy and mAP on Market-1501 respectively.

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Secondly, we use ResNet-101 as basic network instead of ResNet-50 in IDE+

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model. Table 1 shows the effectiveness of ResNet-101 backbone architecture in supervised Re-ID task. On DukeMTMC-reID, using the ResNet-101 as backbone network, IDE* leads to +0.5% and +0.8% improvement over IDE+ in Rank-1 accuracy and mAP, respectively. On Market-1501, the gains are +0.3%

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and +0.8%. Based on the IDE*, in next three part, we discuss the effectiveness

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of multi-scale feature strategy and person semantic enhancement strategy. The effectiveness of the multi-scale feature strategy. In order to prove

the effectiveness of multi-scale feature strategy, based on IDE* model we extract three-scale feature (feature map res3d3, res4b22 and res5c of ResNet101-Original

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Scale branch in MFE model) instead of deep-level feature to implement Re-ID task. Table 1 shows the effectiveness of multi-scale feature strategy(IDE*+MS). On DukeMTMC-reID, based on the original ResNet-101, IDE*+MS leads to +0.9% and +0.6% improvement in Rank-1 accuracy and mAP, respectively. On

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Market-1501, the gains are +0.5% and +0.7%. 19

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The effectiveness of the person semantic enhancement. In this part, we discuss the effectiveness of person semantic enhancement strategy. As shown in Figure 5, person region in person semantic feature extracted from backbone

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of DeepLav V2 model has stronger feature response than other region. In order to prove the effectiveness of the person semantic feature, firstly we extract person semantic feature from backbone of DeepLav V2 model. Secondly, we only

fuse the person semantic feature with deep-level feature to generate enhanced deep-level feature map. Note that multi-scale feature are not used here, and the deep-level feature is usually used to implement Re-ID task. Finally, using

the enhanced deep-level feature map instead of original deep-level feature map

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implements person Re-ID task. Table 1 shows the effectiveness of person semantic feature(IDE*+PF). On DukeMTMC-reID, compared with IDE* based on ResNet-101, IDE*+PF leads to +0.6% and +0.4% improvement in Rank-1 accuracy and mAP, respectively. On Market-1501, the gains are +0.8% and 400

+1.0%.

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The effectiveness of the MFE. In this part, we discuss the effectiveness of MFE Re-ID model. We introduce both person semantic enhancement

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strategy and multi-scale feature strategy into IDE* model, namely MFE Re-ID model. As shown in Figure 5, using person semantic feature to fuse shallow, 405

middle-level and deep-level feature respectively. As we can see that the feature

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response in person region is stronger than that of other region in Multi-scale Enhanced Feature maps. Table 1 shows the effectiveness of MFE Re-ID model

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in supervised Re-ID task. On DukeMTMC-reID, MFE Re-ID model leads to +1.3% and +0.8% improvement over IDE* in Rank-1 accuracy and mAP, re-

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spectively. On Market-1501, the gains are +1.4% and +1.6%. Above all, it is

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proved that MFE Re-ID model could effectively the performance of IDE model in supervised and cross-domain person Re-ID task. 4.4. Ablation Study in FPGAN Image Quality Evaluation. As described in Section 1, we are interested

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in generating a perceptually high-quality image that contain rich person struc20

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Market images to Duke style

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Market images

Duke images to Market style

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Duke images

Figure 9:

Sample images of (upper left:) DukeMTMC-reID dataset, (lower left:) Market-

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1501 dataset, (upper right:) Duke images which are translated to Market style, and (lower right:) Market images translated to Duke style. We use FPGAN for capturing the image style

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of target scene and preseving the person perceptual feature.

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Table 2: Image Quality Evaluation. We calculate the Fr´ echet inception distance (FID) between target images and style transferred images. DukeMTMC-reID→Market-1501: Duke images which are translated to Market style, and Market-1501→DukeMTMC-reID: Market

Method

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images translated to Duke style.

DukeMTMC-reID→Market-1501 Market-1501→DukeMTMC-reID

SPGAN [41]

27.194

FPGAN

24.131

27.736 21.150

ture semantic information and style information of target scene. Image quality

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plays an important role on cross-domain Re-ID task. In cross-domain Re-ID

task, style transferred images need to capture more image-style information about target scene except for preserving person perceptual feature. Thus, we 420

evaluate the image quality about SPGAN [41] and our FPGAN. Here, we use the Fr´echet inception distance (FID) [64] score as the quantitative evaluation metrics. And we calculate the FID between target images and style transferred

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images. Lower FID values mean closer distances between target images and style transferred images. Lower FID values mean that style transferred images 425

capture more image-style information about target scene, which makes Re-ID

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model become more robust to target scene. As shown in Table 2, in two big ReID datasets(DukeMTMC-reID and Market-1501), the FID score of FPGAN is

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lower than that of SPGAN. Besides, examples of translated images by FPGAN are shown in Figure 9. As we can see that transferred images could capture 430

more style information of target scene including background and illumination,

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which is more suitable for cross-domain Re-ID task. Compared with various methods based on GAN. In this part, we

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compare FPGAN with various methods based on GAN in domain adaption Re-ID task, such as PTGAN, CycleGAN, and SPGAN.

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As shown in Table 3 and Table 4, compared with PTGAN, CycleGAN, and

SPGAN, our FPGAN has better peformance in cross-domain Re-ID task on DukeMTMC-reID and Market-1501. Firstly, similar to PTGAN, CycleGAN, and SPGAN, we also set traditional IDE model as our Re-ID model. Table 3

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Table 3: Results of the ablation study of FPGAN and various methods based on GAN in cross-domain Re-ID task on Market-1501 dataset. ”Direct Transfer” means directly applying the source-trained model on the target domain. ”#” means the results directly from the

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corresponding reference.

DukeMTMC-reID→Market-1501

Method

Re-ID model

Supervised Re-ID task

IDE

75.6%

Direct Transfer

IDE

43.2%

CycleGAN

IDE

48.1%

PTGAN# [38]

IDE(GoogLeNet)

38.6%

SPGAN [41]

IDE

50.5%

21.4%

FPGAN

IDE

51.8%

23.2%

FPGAN

IDE (ResNet-101)

52.8%

23.7%

FPGAN+LMP

IDE (ResNet-101)

59.1%

27.1%

FPGAN+LMP+MFE

MFE

64.4%

35.2%

mAP

51.9% 17.3% 20.7% -

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Rank-1

and Table 4 show the effectiveness of FPGAN in domain adaption Re-ID task. On DukeMTMC-reID, FPGAN leads to +1.6% and +1.1% improvement over

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SPGAN in Rank-1 accuracy and mAP, respectively. On Market-1501, the gains are +1.3% and +1.8%. Secondly, we use ResNet-101 as the base network in IDE

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model, namely IDE (ResNet-101). Compared with IDE model, IDE (ResNet101) leads to +2.5% and +1.2% improvement in Rank-1 accuracy and mAP 445

on DukeMTMC-reID, respectively. On Market-1501, the gains are +1.0% and

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+0.5%. And then, we apply Local Max Pooling (LMP)[41, 39] in our approach during testing phase. With LMP, our approach (FPGAN+LMP) gains further

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improvement. Specifically, the Rank-1 and mAP of FPGAN+LMP is higher than FPGAN (IDE(ResNet-101)) by 6.3% and 3.4% respectively, when tested on

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Market-1501. The Rank-1 and mAP of FPGAN+LMP is higher than FPGAN by 6.4% and 4.2% on DukeMTMC-reID, respectively. Finally, we proposed MFE Re-ID model to improve the performance of IDE model in person Re-ID task, and provide a strong baseline model for cross-domain Re-ID task. As shown 23

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Table 4:

Results of the ablation study of FPGAN and various methods based on GAN

in cross-domain Re-ID task on DukeMTMC-reID dataset. ”Direct Transfer” means directly applying the source-trained model on the target domain. ”#” means the results directly from

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the corresponding reference.

Market-1501→DukeMTMC-reID

Method

Re-ID model

Supervised Re-ID task

IDE

66.7%

Direct Transfer(IDE)

IDE

27.6%

CycleGAN

IDE

36.2%

PTGAN# [38]

IDE(GoogLeNet)

27.4%

SPGAN [41]

IDE

37.6%

20.3%

FPGAN

IDE

39.2%

21.4%

FPGAN

IDE (ResNet-101)

41.7%

22.6%

FPGAN+LMP

IDE (ResNet-101)

48.1%

26.8%

FPGAN+LMP+MFE

MFE

52.1%

30.7%

mAP

46.3% 13.3% 19.3% -

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Rank-1

in Table 3 and Table 4, FPGAN+LMP+MFE is higher than FPGAN+LMP by 5.3% and 8.1% respectively, when tested on Market-1501. The Rank-1 and

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mAP of FPGAN+LMP+MFE is higher than FPGAN+LMP by 4.0% and 3.9%

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on DukeMTMC-reID, respectively. 4.5. Comparison with State-of-the-art Methods in unsupervised Re-ID We compare the proposed method with recently state-of-the-art unsupervised learning methods on Market-1501 and DukeMTMC-reID in Table 5 and

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Table 6, respectively.

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We first compare our results with two hand-crafted features, i.e., Bag-of-

Words (BoW) [32] and local maximal occurrence (LOMO) [49]. Those two hand-crafted features are directly applied on test dataset without any training

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process, their inferiority can be clearly observed. Secondly, we compare our method with two unsupervised methods including CAMEL [51], UMDL [52], and SSDAL [24]. These unsupervised methods exploit

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Table 5: Performance comparison with state-of-the-art unsupervised approaches on Market1501. CMC Rank-1 and mAP accuracies are reported. The scores of our proposed methods are shown in bold.

mAP

Bow [32]

35.8%

14.8%

LOMO [49]

27.2%

8.0%

SSDAL [24]

39.4%

-

CAMEL [51]

54.5%

-

SPGAN [41]

50.5%

21.4%

SPGAN+LMP [41]

57.7%

26.9%

PTGAN [38] TJ-AIDL [14] TF-Fusion [65] DATS[40] FPGAN

2015 ICCV

2015 CVPR 2016 CVPR 2017 ICCV

2018 CVPR 2018 CVPR

38.6%

-

2018 CVPR

58.2%

26.5%

2018 CVPR

58.2%

-

2018 CVPR

65.7%

-

2018 ECCV

52.8%

23.7%

Proposed

59.1%

27.1%

Proposed

64.4%

35.2%

Proposed

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FPGAN+LMP

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Rank-1

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FPGAN+LMP+MFE

the unlabeled data on target domain for training Re-ID model and achieve higher results than hand-crafted methods. Finally, we compare our method with recently proposed state-of-the-art do-

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main adaptation methods, including the SSDAL [24], CAMEL [51], TJ-AIDL

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[14], SPGAN [41],TF-Fusion [65], PTGAN [38], and DATS[40]. On Market1501, our method achieves Rank-1 accuracy = 64.4% and mAP = 35.2% in Rank-1 accuracy and mAP, respectively. on DukeMTMC-reID, our method achieves Rank-1 accuracy = 52.1% and mAP = 30.7% in Rank-1 accuracy and

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mAP, respectively. Our method obtains competitive results compared with the state-of-the-art approaches.

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Table 6:

Performance comparison with state-of-the-art unsupervised approaches on

DukeMTMC-reID. CMC Rank-1 and mAP accuracies are reported. The scores of our proposed methods are shown in bold.

mAP

Bow [32]

17.1%

8.3%

LOMO [49]

12.3%

4.8%

UMDL [52]

18.5%

7.3%

PTGAN [38]

27.4%

-

SPGAN [41]

37.6%

20.3%

SPGAN+LMP [41]

46.4%

26.2%

TJ-AIDL [14] FPGAN FPGAN+LMP FPGAN+LMP+MFE

Reference

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Rank-1

2015 ICCV

2015 CVPR 2016 CVPR 2018 CVPR 2018 CVPR 2018 CVPR

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Method

44.3%

23.0%

2018 CVPR

41.7%

22.6%

Proposed

48.1%

26.8%

Proposed

52.1%

30.7%

Proposed

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4.6. Comparison with State-of-the-art Methods in supervised Re-ID In unsupervised Re-ID task, our method has achieved state-of-the-art result. Besides, our approach is also very competitive with a series of state-of-the-

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art supervised techniques. In supervised Re-ID task, we train MFE Re-ID model in training set of two big datasets, and implement person Re-ID task

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on testing set respectively. We also compare the proposed method with the state-of the-art supervised learning methods on Market-1501 and DukeMTMCreID in Table 7 and Table 8, respectively. Experimental results show that our

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method achieves state-of-the-art results through multi-scale feaure enhancement strategy enhancing the feature response of the human body part. Specifically,

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we achieve rank-1 accuracy = 95.8% for Market-1501, and rank-1 accuracy = 88.7% for DukeMTMC-reID. And we achieve mAP = 87.6% for Market-1501,

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and mAP= 77.9% for DukeMTMC-reID. All experimental results show that our MFE Re-ID model also achieves state-of-the-art results in supervised Re-ID task.

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Table 7: Performance comparison with state-of-the-art supervised techniques on Market-1501. CMC Rank-1 and mAP accuracies are reported. The scores of our proposed methods are shown in bold.

Rank-1

mAP

Point-to-Set [27]

70.7%

44.3%

CCAFA [7]

71.8%

45.5%

Consistent-Aware [28]

73.8%

47.1%

Spindle [29]

76.9%

-

HydraPlus-Net [17]

76.9%

-

Re-ranking[30]

77.1%

63.6%

GAN [66]

78.1%

56.2%

2017 ICCV

MSCAN [67]

80.3%

57.5%

2017 CVPR

DLPAR [68]

81%

63.4%

2017 ICCV

Scalable [69]

82.2%

68.8%

2017 CVPR

SVDNet [70]

82.3%

62.1%

2017 ICCV

MCAM[57]

83.8%

74.3%

2018 CVPR

88.1%

68.7%

2019 TIP

92.5%

81.3%

2018 CVPR

PCB [73]

93.8%

81.6%

2018 ECCV

Mancs[74]

93.1%

82.3%

2018 ECCV

S-Baseline[63]

94.1%

85.2%

2019 CVPR

95.8%

87.6%

Proposed

PT

2017 TPAMI 2017 CVPR 2017 CVPR 2017 ICCV

2017 CVPR

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MFE

2017 CVPR

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SPReID[72]

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Camstyle[71]

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5. Conclusion This paper focuses on cross-domain person Re-ID model. In order to improve

the performance of the Re-ID model on target dataset, Firstly, we proposed

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MFE Re-ID model to provide a strong baseline Re-ID model for cross-domain person Re-ID task. In MFE model, multi-scale feaure enhancement strategy could enhance the multi-scale person feature response, which could reduce the interference of background and capture robust multi-scale discriminative visual

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Table 8: Performance comparison with state-of-the-art supervised techniques on DukeMTMCreID. CMC Rank-1 and mAP accuracies are reported. The scores of our proposed methods are shown in bold.

mAP

GAN [66]

67.7%

47.1%

OIM [75]

68.1%

-

APR [76]

70.7%

51.9%

TriNet [77]

72.4%

53.5%

SVDNet [70]

76.7%

56.8%

DPFL [78]

79.2%

60.6%

JLML [79]

2017 ICCV

2017 CVPR 2017 arXiv 2017 arXiv

2017 ICCV 2017 ICCV

73.3%

56.4%

2017 IJCAI

71.6%

51.5%

2018 TCSVT

75.3%

53.5%

2019 TIP

78.3%

57.6%

2018 CVPR

80.5%

63.8%

2018 CVPR

83.3%

69.2%

2018 ECCV

84.9%

71.8%

2018 ECCV

84.4%

71.0%

2018 CVPR

S-Baseline[63]

86.9%

76.3%

2019 CVPR

MFE

88.7%

77.9%

Proposed

PAN [80] Camstyle[71] IDE+CamStyle+RE [39]

PCB [73] Mancs[74]

PT

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SPReID[72]

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HA-CNN [9]

500

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Rank-1

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factors. MFE Re-ID model plays an important role in cross-domain and super-

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vised Re-ID task. Based on MFE Re-ID model, secondly we proposed FPGAN. During the image-image translation, FPGAN could effectively preserve the highlevel perceptual information of source images and ensure the transferred person

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images show similar styles with the target dataset. Experiment shows that FP-

505

GAN could better qualify the generated images for domain adaptation. On two source-target settings, FPGAN has good performance on cross-domain person Re-ID task. Overall, FPGAN and MFE Re-ID model achieve good performance in cross-domain and supervised person Re-ID task.

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6. Acknowledgements This work is supported by National Natural Science Foundation of China

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(U1811463). We thank the anonymous reviewers for the insightful and con-

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structive comments. We thank all authors finish this research and complete the writing of the paper. No conflict of interest: Xiuping Liu, Hongchen Tan, Xin Tong, Junjie Cao and Jun Zhou declare that they have no conflict of interest.

Conflict Of Interest Have no conflict of interest

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Video Technology (TCSVT)doi:10.1109/tcsvt.2018.2873599.

Xiuping Liu is a Professor in School of Mathematical Sciences at Dalian

University of Technology, P.R. China. She received Ph.D degrees in computa-

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tional mathematics from Dalian University of Technology. Her research interests include shape modeling and analyzing.

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Hongchen Tan is doctor candidate of Mathematical Sciences at Dalian University of Technology. His research interest is object detection, person Reidentification and Cross-modal Retrieval.

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Xin Tong is a principal researcher in Internet Graphics Group of Microsoft Research Asia. His research interests include appearance modeling and render-

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ing, texture synthesis, and image based modeling and rendering. Specifically, his research concentrates on studying the underline principles of material light

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interaction and light transport, and developing efficient methods for appearance modeling and rendering. He is also interested in performance capturing

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and facial animation.

Junjie Cao is a lecturer in School of Mathematical Sciences at Dalian Uni-

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versity of Technology, P.R. China. He received Ph.D degrees in computational mathematics from Dalian University of Technology. His research interests include shape modeling, image processing and machine learning.

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Jun Zhou is a Ph.D. Candidate in School of Mathematical Sciences at

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Dalian University of Technology, P.R. China. He received the B.S. in Information and Computing Science from Dalian University of Technology. His research

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interests include computer graphics, image processing, machine learning.

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