On Exploring Pose Estimation as an Auxiliary Learning Task for Visible-Infrared Person Re-identification

• URL: http://arxiv.org/abs/2201.03859v1
• Date: Tue, 11 Jan 2022 09:44:00 GMT
• Title: On Exploring Pose Estimation as an Auxiliary Learning Task for Visible-Infrared Person Re-identification
• Authors: Yunqi Miao, Nianchang Huang, Xiao Ma, Qiang Zhang, and Jungong Han
• Abstract summary: In this paper, we exploit Pose Estimation as an auxiliary learning task to assist the VI-ReID task in an end-to-end framework. By jointly training these two tasks in a mutually beneficial manner, our model learns higher quality modality-shared and ID-related features. Experimental results on two benchmark VI-ReID datasets show that the proposed method consistently improves state-of-the-art methods by significant margins.
• Score: 66.58450185833479
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visible-infrared person re-identification (VI-ReID) has been challenging due to the existence of large discrepancies between visible and infrared modalities. Most pioneering approaches reduce intra-class variations and inter-modality discrepancies by learning modality-shared and ID-related features. However, an explicit modality-shared cue, i.e., body keypoints, has not been fully exploited in VI-ReID. Additionally, existing feature learning paradigms imposed constraints on either global features or partitioned feature stripes, which neglect the prediction consistency of global and part features. To address the above problems, we exploit Pose Estimation as an auxiliary learning task to assist the VI-ReID task in an end-to-end framework. By jointly training these two tasks in a mutually beneficial manner, our model learns higher quality modality-shared and ID-related features. On top of it, the learnings of global features and local features are seamlessly synchronized by Hierarchical Feature Constraint (HFC), where the former supervises the latter using the knowledge distillation strategy. Experimental results on two benchmark VI-ReID datasets show that the proposed method consistently improves state-of-the-art methods by significant margins. Specifically, our method achieves nearly 20$\%$ mAP improvements against the state-of-the-art method on the RegDB dataset. Our intriguing findings highlight the usage of auxiliary task learning in VI-ReID.

Related papers

• Deep Common Feature Mining for Efficient Video Semantic Segmentation [29.054945307605816]
  We present Deep Common Feature Mining (DCFM) for video semantic segmentation. DCFM explicitly decomposes features into two complementary components. We show that our method has a superior balance between accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-03-05T06:17:59Z)
• Learning Cross-modality Information Bottleneck Representation for Heterogeneous Person Re-Identification [61.49219876388174]
  Visible-Infrared person re-identification (VI-ReID) is an important and challenging task in intelligent video surveillance. Existing methods mainly focus on learning a shared feature space to reduce the modality discrepancy between visible and infrared modalities. We present a novel mutual information and modality consensus network, namely CMInfoNet, to extract modality-invariant identity features.
  arXiv  Detail & Related papers  (2023-08-29T06:55:42Z)
• Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement [53.044703127757295]
  Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims at learning modality-invariant features from unlabeled cross-modality dataset. We propose a Dual Optimal Transport Label Assignment (DOTLA) framework to simultaneously assign the generated labels from one modality to its counterpart modality. The proposed DOTLA mechanism formulates a mutual reinforcement and efficient solution to cross-modality data association, which could effectively reduce the side-effects of some insufficient and noisy label associations.
  arXiv  Detail & Related papers  (2023-05-22T04:40:30Z)
• Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification [90.39454748065558]
  Body shape is one of the significant modality-shared cues for VI-ReID. We propose shape-erased feature learning paradigm that decorrelates modality-shared features in two subspaces. Experiments on SYSU-MM01, RegDB, and HITSZ-VCM datasets demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2023-04-09T10:22:10Z)
• Learning Progressive Modality-shared Transformers for Effective Visible-Infrared Person Re-identification [27.75907274034702]
  We propose a novel deep learning framework named Progressive Modality-shared Transformer (PMT) for effective VI-ReID. To reduce the negative effect of modality gaps, we first take the gray-scale images as an auxiliary modality and propose a progressive learning strategy. To cope with the problem of large intra-class differences and small inter-class differences, we propose a Discriminative Center Loss.
  arXiv  Detail & Related papers  (2022-12-01T02:20:16Z)
• Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition [36.350407471391065]
  We propose two auxiliary AU related tasks to bridge the gap between limited annotations and the model performance. A single image based optical flow estimation task is proposed to leverage the dynamic change of facial muscles. By incorporating semi-supervised learning, we propose an end-to-end trainable framework named weakly supervised regional and temporal learning.
  arXiv  Detail & Related papers  (2022-04-01T12:02:01Z)
• G$^2$DA: Geometry-Guided Dual-Alignment Learning for RGB-Infrared Person Re-Identification [3.909938091041451]
  RGB-IR person re-identification aims to retrieve person-of-interest between heterogeneous modalities. This paper presents a Geometry-Guided Dual-Alignment learning framework (G$2$DA) to tackle sample-level modality difference.
  arXiv  Detail & Related papers  (2021-06-15T03:14:31Z)
• Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification [208.1227090864602]
  Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. Existing VI-ReID methods tend to learn global representations, which have limited discriminability and weak robustness to noisy images. We propose a novel dynamic dual-attentive aggregation (DDAG) learning method by mining both intra-modality part-level and cross-modality graph-level contextual cues for VI-ReID.
  arXiv  Detail & Related papers  (2020-07-18T03:08:13Z)
• Cross-modality Person re-identification with Shared-Specific Feature Transfer [112.60513494602337]
  Cross-modality person re-identification (cm-ReID) is a challenging but key technology for intelligent video analysis. We propose a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modality-specific characteristics.
  arXiv  Detail & Related papers  (2020-02-28T00:18:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.