Visible-infrared Person Re-identification via Colorization-based Siamese Generative Adversarial Network

Zhong, Xian; Lu, Tianyou; Huang, Wenxin; Yuan, Jingling; Liu, Wenxuan; Lin, Chia-Wen

doi:10.1145/3372278.3390696

Cited by 26 publications

(12 citation statements)

References 23 publications

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“…To our knowledge, there is little work in the literature using colorization. Zhong et al [53] bridged the gap between the two modalities by fusing the features of original infrared images and generated fake visible images. After that, Zhong et al [11] improved the performance by pixel-wise transformation, which can retain original structure information.…”

Section: Modality Translationmentioning

confidence: 99%

Visible-Infrared Person Re-Identification: A Comprehensive Survey and a New Setting

et al. 2022

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Person re-identification (ReID) plays a crucial role in video surveillance with the aim to search a specific person across disjoint cameras, and it has progressed notably in recent years. However, visible cameras may not be able to record enough information about the pedestrian’s appearance under the condition of low illumination. On the contrary, thermal infrared images can significantly mitigate this issue. To this end, combining visible images with infrared images is a natural trend, and are considerably heterogeneous modalities. Some attempts have recently been contributed to visible-infrared person re-identification (VI-ReID). This paper provides a complete overview of current VI-ReID approaches that employ deep learning algorithms. To align with the practical application scenarios, we first propose a new testing setting and systematically evaluate state-of-the-art methods based on our new setting. Then, we compare ReID with VI-ReID in three aspects, including data composition, challenges, and performance. According to the summary of previous work, we classify the existing methods into two categories. Additionally, we elaborate on frequently used datasets and metrics for performance evaluation. We give insights on the historical development and conclude the limitations of off-the-shelf methods. We finally discuss the future directions of VI-ReID that the community should further address.

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Section: Modality Translationmentioning

confidence: 99%

Visible-Infrared Person Re-Identification: A Comprehensive Survey and a New Setting

et al. 2022

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“…Recently developed generative adversarial technique provides a powerful tool for image translation. The work most relevant to ours are following five image-based methods [25], [14], [15], [16], [10]. XIV cross-modality learning method was proposed in [25].…”

Section: Related Workmentioning

confidence: 99%

“…AlignGAN [10] is the first work to adopt the pixel-level and feature-level alignment strategies in a unified framework, which not only reduce the cross-modality and intra-modality discrepancy, but also learn identity-consitent features. Meanwhile, some other works [15], [16] attempt to use GANs to generate more realistic cross-modality images to eliminate large modality discrenpancy. All these methods achieve superior performance, but training a great generator and a discriminator would cost huge computing resources and how to balance the generator and discriminator is an intractable problem.…”

Section: Related Workmentioning

confidence: 99%

“…We compare the performance of our proposed approach (GMA) with other state-of-the-art VI-ReID methods on SYSU-MM01 and RegDB in Table V and Table VII, respectively. A variety of existing VI-ReID models are included for comparision, containing the up-to-date approachs of X-Modal [25], eDBTR [24], MSR [23], CosiGAN [15], MACE [33], DDAG [34], and cm-SSFT [34] that published in 2020. In addition, some learning-based classic VI-ReID methods are also used for comparisons, such as Zero-Pad [5], HSME [35], cmGAN [13], D 2 RL [14], HPILN [31], LZM [32] and AlignG [10].…”

Section: Comparison With State-of-the-artsmentioning

confidence: 99%

“…However, these methods only consider the modality discrepancy of global coarse-grained features, but ignore the variation of pixellevel fine-grained features. More recently, some GAN-based methods [10], [14], [16], [15] have been proposed to generate cross-modality images to fill the gap between two different modalities. However, GAN based model training is unstable and the key local structure information of images is easily destroyed with unsupervised generation manners.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification

Liu¹,

Ma²,

Xia³

et al. 2021

Preprint

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Enhancing Invariant Feature Learning via Cross‐Composition and Self‐Enrichment Normalization for Visible‐Infrared Person Re‐Identification

Zhang

2024

Concurrency and Computation

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Visible‐Infrared Person Re‐Identification (VI‐ReID) is a complex challenge in cross‐modality retrieval, wherein the goal is to recognize individuals from images captured via RGB and IR cameras. While many existing methods focus on narrowing the gap between different modalities through designing various feature‐level constraints, they often neglect the consistency of channel statistics information across the modalities, which results in suboptimal matching performance. In this work, we introduce a new approach for VI‐ReID that incorporates Cross‐Composition Normalization (CCN) and Self‐Enrichment Normalization (SEN). Specifically, Cross‐Composition Normalization is a plug‐and‐play module that can be seamlessly integrated into shallow CNN layers without requiring modifications to the training objectives. It probabilistically blends feature statistics between instances, thereby fostering the model's ability to learn inter‐modality feature distributions. Conversely, Self‐Enrichment Normalization leverages attention mechanisms to recalibrate statistics, effectively bridging the gaps between training and test distributions. This enhancement markedly boosts the discriminability of features in VI‐ReID tasks. To validate the efficacy of our proposed method, we carried out comprehensive experiments on two public cross‐modality datasets. The results clearly demonstrate the superiority of our Cross‐Composition and Self‐Enrichment normalization techniques in addressing the challenges of the VI‐ReID problem.

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Visible-infrared Person Re-identification via Colorization-based Siamese Generative Adversarial Network

Cited by 26 publications

References 23 publications

Visible-Infrared Person Re-Identification: A Comprehensive Survey and a New Setting

Visible-Infrared Person Re-Identification: A Comprehensive Survey and a New Setting

SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification

Enhancing Invariant Feature Learning via Cross‐Composition and Self‐Enrichment Normalization for Visible‐Infrared Person Re‐Identification

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