Region Generation and Assessment Network for Occluded Person Re-Identification

• URL: http://arxiv.org/abs/2309.03558v1
• Date: Thu, 7 Sep 2023 08:41:47 GMT
• Title: Region Generation and Assessment Network for Occluded Person Re-Identification
• Authors: Shuting He, Weihua Chen, Kai Wang, Hao Luo, Fan Wang, Wei Jiang, Henghui Ding
• Abstract summary: Person Re-identification (ReID) plays a more and more crucial role in recent years with a wide range of applications. Most methods tackle such challenges by utilizing external tools to locate body parts or exploiting matching strategies. We propose a Region Generation and Assessment Network (RGANet) to effectively and efficiently detect the human body regions.
• Score: 43.49129366128688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person Re-identification (ReID) plays a more and more crucial role in recent years with a wide range of applications. Existing ReID methods are suffering from the challenges of misalignment and occlusions, which degrade the performance dramatically. Most methods tackle such challenges by utilizing external tools to locate body parts or exploiting matching strategies. Nevertheless, the inevitable domain gap between the datasets utilized for external tools and the ReID datasets and the complicated matching process make these methods unreliable and sensitive to noises. In this paper, we propose a Region Generation and Assessment Network (RGANet) to effectively and efficiently detect the human body regions and highlight the important regions. In the proposed RGANet, we first devise a Region Generation Module (RGM) which utilizes the pre-trained CLIP to locate the human body regions using semantic prototypes extracted from text descriptions. Learnable prompt is designed to eliminate domain gap between CLIP datasets and ReID datasets. Then, to measure the importance of each generated region, we introduce a Region Assessment Module (RAM) that assigns confidence scores to different regions and reduces the negative impact of the occlusion regions by lower scores. The RAM consists of a discrimination-aware indicator and an invariance-aware indicator, where the former indicates the capability to distinguish from different identities and the latter represents consistency among the images of the same class of human body regions. Extensive experimental results for six widely-used benchmarks including three tasks (occluded, partial, and holistic) demonstrate the superiority of RGANet against state-of-the-art methods.

Related papers

• Spatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification [32.95715593278961]
  Unsupervised person re-identification (re-ID) methods achieve high performance by leveraging fine-grained local context. Part-based methods obtain local contexts through horizontal division, which suffer from misalignment due to various human poses. We introduce the Spatial Cascaded Clustering and Weighted Memory (SCWM) method to address these challenges. SCWM aims to parse and align more accurate local contexts for different human body parts while allowing the memory module to balance hard example mining and noise suppression.
  arXiv  Detail & Related papers  (2024-03-01T03:52:29Z)
• Cross Domain Early Crop Mapping using CropSTGAN [12.271756709807898]
  This paper introduces the Crop Mapping Spectral-temporal Generative Adrial Neural Network (CropSTGAN) CropSTGAN learns to transform the target domain's spectral features to those of the source domain, effectively bridging large dissimilarities. In experiments, CropSTGAN is benchmarked against various state-of-the-art (SOTA) methods.
  arXiv  Detail & Related papers  (2024-01-15T00:27:41Z)
• Deep face recognition with clustering based domain adaptation [57.29464116557734]
  We propose a new clustering-based domain adaptation method designed for face recognition task in which the source and target domain do not share any classes. Our method effectively learns the discriminative target feature by aligning the feature domain globally, and, at the meantime, distinguishing the target clusters locally.
  arXiv  Detail & Related papers  (2022-05-27T12:29:11Z)
• Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation [19.584457251137252]
  We propose a method called region-aware metric learning (RAML) RAML separates the regions of the images and generates region-aware features for further metric learning. We show that the proposed RAML achieves SOTA performance in both stages of open world segmentation.
  arXiv  Detail & Related papers  (2022-05-17T04:12:47Z)
• Region-Based Semantic Factorization in GANs [67.90498535507106]
  We present a highly efficient algorithm to factorize the latent semantics learned by Generative Adversarial Networks (GANs) concerning an arbitrary image region. Through an appropriately defined generalized Rayleigh quotient, we solve such a problem without any annotations or training. Experimental results on various state-of-the-art GAN models demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2022-02-19T17:46:02Z)
• 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)
• Learning Domain Invariant Representations for Generalizable Person Re-Identification [71.35292121563491]
  Generalizable person Re-Identification (ReID) has attracted growing attention in recent computer vision community. We introduce causality into person ReID and propose a novel generalizable framework, named Domain Invariant Representations for generalizable person Re-Identification (DIR-ReID)
  arXiv  Detail & Related papers  (2021-03-29T18:59:48Z)
• Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection [121.28769542994664]
  Domain adaptation for object detection tries to adapt the detector from labeled datasets to unlabeled ones for better performance. In this paper, we are the first to reveal that the region proposal network (RPN) and region proposal classifier(RPC) demonstrate significantly different transferability when facing large domain gap.
  arXiv  Detail & Related papers  (2020-09-17T07:39:52Z)
• Adaptive Deep Metric Embeddings for Person Re-Identification under Occlusions [17.911512103472727]
  We propose a novel person ReID method, which learns the spatial dependencies between the local regions and extracts the discriminative feature representation of the pedestrian image based on Long Short-Term Memory (LSTM) The proposed loss enables the deep neural network to adaptively learn discriminative metric embeddings, which significantly improve the capability of recognizing unseen person identities.
  arXiv  Detail & Related papers  (2020-02-07T03:18:10Z)
• Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis [76.46004354572956]
  We introduce an unsupervised domain adaptation approach for person re-identification. Experimental results show that the proposed ktCUDA and SHRED approach achieves an average improvement of +5.7 mAP in re-identification performance.
  arXiv  Detail & Related papers  (2020-01-14T17:43:52Z)

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.