Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identification

• URL: http://arxiv.org/abs/2410.15613v2
• Date: Wed, 23 Oct 2024 13:07:41 GMT
• Title: Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identification
• Authors: Zhangjian Ji, Donglin Cheng, Kai Feng,
• Abstract summary: We proposed a novel self-supervision and supervision combining transformer-based person re-identification framework, namely SSSC-TransReID. We designed a self-supervised contrastive learning branch, which can enhance the feature representation for person re-identification without negative samples or additional pre-training. Our proposed model obtains superior Re-ID performance consistently and outperforms the state-of-the-art ReID methods by large margins on the mean average accuracy (mAP) and Rank-1 accuracy.
• Score: 2.552131151698595
• License:
• Abstract: Due to some complex factors (e.g., occlusion, pose variation and diverse camera perspectives), extracting stronger feature representation in person re-identification remains a challenging task. In this paper, we proposed a novel self-supervision and supervision combining transformer-based person re-identification framework, namely SSSC-TransReID. Different from the general transformer-based person re-identification models, we designed a self-supervised contrastive learning branch, which can enhance the feature representation for person re-identification without negative samples or additional pre-training. In order to train the contrastive learning branch, we also proposed a novel random rectangle mask strategy to simulate the occlusion in real scenes, so as to enhance the feature representation for occlusion. Finally, we utilized the joint-training loss function to integrate the advantages of supervised learning with ID tags and self-supervised contrastive learning without negative samples, which can reinforce the ability of our model to excavate stronger discriminative features, especially for occlusion. Extensive experimental results on several benchmark datasets show our proposed model obtains superior Re-ID performance consistently and outperforms the state-of-the-art ReID methods by large margins on the mean average accuracy (mAP) and Rank-1 accuracy.

Related papers

• Face Forgery Detection with Elaborate Backbone [50.914676786151574]
  Face Forgery Detection aims to determine whether a digital face is real or fake. Previous FFD models directly employ existing backbones to represent and extract forgery cues. We propose leveraging the ViT network with self-supervised learning on real-face datasets to pre-train a backbone. We then build a competitive backbone fine-tuning framework that strengthens the backbone's ability to extract diverse forgery cues.
  arXiv  Detail & Related papers  (2024-09-25T13:57:16Z)
• PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings [55.55445978692678]
  PseudoNeg-MAE is a self-supervised learning framework that enhances global feature representation of point cloud mask autoencoders. We show that PseudoNeg-MAE achieves state-of-the-art performance on the ModelNet40 and ScanObjectNN datasets.
  arXiv  Detail & Related papers  (2024-09-24T07:57:21Z)
• Robust Ensemble Person Re-Identification via Orthogonal Fusion with Occlusion Handling [4.431087385310259]
  Occlusion remains one of the major challenges in person reidentification (ReID) We propose a deep ensemble model that harnesses both CNN and Transformer architectures to generate robust feature representations.
  arXiv  Detail & Related papers  (2024-03-29T18:38:59Z)
• Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems [61.11799513362704]
  We propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective.
  arXiv  Detail & Related papers  (2023-03-03T02:07:40Z)
• Learning Invariance from Generated Variance for Unsupervised Person Re-identification [15.096776375794356]
  We propose to replace traditional data augmentation with a generative adversarial network (GAN) A 3D mesh guided person image generator is proposed to disentangle a person image into id-related and id-unrelated features. By jointly training the generative and the contrastive modules, our method achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.
  arXiv  Detail & Related papers  (2023-01-02T15:40:14Z)
• 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)
• R\'enyiCL: Contrastive Representation Learning with Skew R\'enyi Divergence [78.15455360335925]
  We present a new robust contrastive learning scheme, coined R'enyiCL, which can effectively manage harder augmentations. Our method is built upon the variational lower bound of R'enyi divergence. We show that R'enyi contrastive learning objectives perform innate hard negative sampling and easy positive sampling simultaneously.
  arXiv  Detail & Related papers  (2022-08-12T13:37:05Z)
• Feature Diversity Learning with Sample Dropout for Unsupervised Domain Adaptive Person Re-identification [0.0]
  This paper proposes a new approach to learn the feature representation with better generalization ability through limiting noisy pseudo labels. We put forward a brand-new method referred as to Feature Diversity Learning (FDL) under the classic mutual-teaching architecture. Experimental results show that our proposed FDL-SD achieves the state-of-the-art performance on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2022-01-25T10:10:48Z)
• Joint Generative and Contrastive Learning for Unsupervised Person Re-identification [15.486689594217273]
  Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework.
  arXiv  Detail & Related papers  (2020-12-16T16:49:57Z)
• Fully Unsupervised Person Re-identification viaSelective Contrastive Learning [58.5284246878277]
  Person re-identification (ReID) aims at searching the same identity person among images captured by various cameras. We propose a novel selective contrastive learning framework for unsupervised feature learning. Experimental results demonstrate the superiority of our method in unsupervised person ReID compared with the state-of-the-arts.
  arXiv  Detail & Related papers  (2020-10-15T09:09:23Z)
• Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond [59.149653740463435]
  Person re-identification (re-ID) aims at matching images of the same person across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected. We propose a novel generative adversarial network to address cross-resolution person re-ID.
  arXiv  Detail & Related papers  (2020-02-19T07:21:38Z)

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.