Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-identification

• URL: http://arxiv.org/abs/2303.10976v2
• Date: Thu, 22 Feb 2024 08:24:45 GMT
• Title: Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-identification
• Authors: Jiaer Xia, Lei Tan, Pingyang Dai, Mingbo Zhao, Yongjian Wu, Liujuan Cao
• Abstract summary: We propose a transformer-based Attention Disturbance and Dual-Path Constraint Network (ADP) to enhance the generalization of attention networks. To imitate real-world obstacles, we introduce an Attention Disturbance Mask (ADM) module that generates an offensive noise. We also develop a Dual-Path Constraint Module (DPC) that can obtain preferable supervision information from holistic images.
• Score: 36.86516784815214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Occluded person re-identification (Re-ID) aims to address the potential occlusion problem when matching occluded or holistic pedestrians from different camera views. Many methods use the background as artificial occlusion and rely on attention networks to exclude noisy interference. However, the significant discrepancy between simple background occlusion and realistic occlusion can negatively impact the generalization of the network. To address this issue, we propose a novel transformer-based Attention Disturbance and Dual-Path Constraint Network (ADP) to enhance the generalization of attention networks. Firstly, to imitate real-world obstacles, we introduce an Attention Disturbance Mask (ADM) module that generates an offensive noise, which can distract attention like a realistic occluder, as a more complex form of occlusion. Secondly, to fully exploit these complex occluded images, we develop a Dual-Path Constraint Module (DPC) that can obtain preferable supervision information from holistic images through dual-path interaction. With our proposed method, the network can effectively circumvent a wide variety of occlusions using the basic ViT baseline. Comprehensive experimental evaluations conducted on person re-ID benchmarks demonstrate the superiority of ADP over state-of-the-art methods.

Related papers

• Pose-Transformation and Radial Distance Clustering for Unsupervised Person Re-identification [5.522856885199346]
  Person re-identification (re-ID) aims to tackle the problem of matching identities across non-overlapping cameras. Supervised approaches require identity information that may be difficult to obtain and are inherently biased towards the dataset they are trained on. We propose an unsupervised approach to the person re-ID setup. Having zero knowledge of true labels, our proposed method enhances the discriminating ability of the learned features.
  arXiv  Detail & Related papers  (2024-11-06T20:55:30Z)
• Deep Generative Adversarial Network for Occlusion Removal from a Single Image [3.5639148953570845]
  We propose a fully automatic, two-stage convolutional neural network for fence segmentation and occlusion completion. We leverage generative adversarial networks (GANs) to synthesize realistic content, including both structure and texture, in a single shot for inpainting.
  arXiv  Detail & Related papers  (2024-09-20T06:00:45Z)
• DPMesh: Exploiting Diffusion Prior for Occluded Human Mesh Recovery [71.6345505427213]
  DPMesh is an innovative framework for occluded human mesh recovery. It capitalizes on the profound diffusion prior about object structure and spatial relationships embedded in a pre-trained text-to-image diffusion model.
  arXiv  Detail & Related papers  (2024-04-01T18:59:13Z)
• Occlusion-Aware Detection and Re-ID Calibrated Network for Multi-Object Tracking [38.36872739816151]
  Occlusion-Aware Attention (OAA) module in the detector highlights the object features while suppressing the occluded background regions. OAA can serve as a modulator that enhances the detector for some potentially occluded objects. We design a Re-ID embedding matching block based on the optimal transport problem.
  arXiv  Detail & Related papers  (2023-08-30T06:56:53Z)
• Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification [36.91680117072686]
  We propose Erasing, Transforming, and Noising Defense Network (ETNDNet) to solve occluded person re-ID. In the proposed ETNDNet, we randomly erase the feature map to create an adversarial representation with incomplete information. Thirdly, we perturb the feature map with random values to address noisy information introduced by obstacles and non-target pedestrians.
  arXiv  Detail & Related papers  (2023-07-14T06:42:21Z)
• Learning Feature Recovery Transformer for Occluded Person Re-identification [71.18476220969647]
  We propose a new approach called Feature Recovery Transformer (FRT) to address the two challenges simultaneously. To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity. In terms of the second challenge, based on the developed graph similarity, for each query image, we propose a recovery transformer that exploits the feature sets of its $k$-nearest neighbors in the gallery to recover the complete features.
  arXiv  Detail & Related papers  (2023-01-05T02:36:16Z)
• Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning [8.015703163954639]
  Occluded person re-identification (Re-ID) in images captured by multiple cameras is challenging because the target person is occluded by pedestrians or objects. Most existing methods utilize the off-the-shelf pose or parsing networks as pseudo labels, which are prone to error. We propose a novel Occlusion Correction Network (OCNet) that corrects features through relational-weight learning and obtains diverse and representative features without using external networks.
  arXiv  Detail & Related papers  (2022-12-09T07:48:47Z)
• Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning [54.15303628138665]
  Face anti-spoofing (FAS) plays a vital role in preventing face recognition systems from presentation attacks. Existing face anti-spoofing datasets lack diversity due to the insufficient identity and insignificant variance. We propose Dual Spoof Disentanglement Generation framework to tackle this challenge by "anti-spoofing via generation"
  arXiv  Detail & Related papers  (2021-12-01T15:36:59Z)
• Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians [97.45805377769354]
  This paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet) PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods.
  arXiv  Detail & Related papers  (2020-08-16T17:45:14Z)
• DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection [107.96418568008644]
  We propose a novel network named DPANet to explicitly model the potentiality of the depth map and effectively integrate the cross-modal complementarity. By introducing the depth potentiality perception, the network can perceive the potentiality of depth information in a learning-based manner.
  arXiv  Detail & Related papers  (2020-03-19T07:27:54Z)

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.