Attribute Guidance With Inherent Pseudo-label For Occluded Person Re-identification
Person re-identification (Re-ID) aims to match person images across different camera views, with occluded Re-ID addressing scenarios where pedestrians are partially visible. While pre-trained vision-language models have shown effectiveness in Re-ID tasks, they face significant challenges in occluded scenarios by focusing on holistic image semantics while neglecting fine-grained attribute information. This limitation becomes particularly evident when dealing with partially occluded pedestrians or when distinguishing between individuals with subtle appearance differences. To address this limitation, we propose Attribute-Guide ReID (AG-ReID), a novel framework that leverages pre-trained models' inherent capabilities to extract fine-grained semantic attributes without additional data or annotations. Our framework operates through a two-stage process: first generating attribute pseudo-labels that capture subtle visual characteristics, then introducing a dual-guidance mechanism that combines holistic and fine-grained attribute information to enhance image feature extraction. Extensive experiments demonstrate that AG-ReID achieves state-of-the-art results on multiple widely-used Re-ID datasets, showing significant improvements in handling occlusions and subtle attribute differences while maintaining competitive performance on standard Re-ID scenarios.
Code (0)
등록된 구현이 없습니다.
Tasks
Person Re-IdentificationSimilar Papers 제목 키워드 기반
A Pedestrian is Worth One Prompt: Towards Language Guidance Person Re-Identification
Extensive advancements have been made in person ReID through the mining of semantic information. Nevertheless existing methods that utilize semantic-parts from a single image modality do not explicitly achieve this g…
AttributePerson Re-IdentificationPrompt EngineeringUnsupervised Domain Adaptation for Occlusion Resilient Human Pose Estimation
Occlusions are a significant challenge to human pose estimation algorithms, often resulting in inaccurate and anatomically implausible poses. Although current occlusion-robust human pose estimation algorithms exhibit imp…
Domain AdaptationPose EstimationPose PredictionPseudo Label+1Unsupervised Stereo via Multi-Baseline Geometry-Consistent Self-Training
Photometric loss and pseudo-label-based self-training are two widely used methods for training stereo networks on unlabeled data. However, they both struggle to provide accurate supervision in occluded regions. The forme…
Tracking-Assisted Object Detection with Event Cameras
Event-based object detection has recently garnered attention in the computer vision community due to the exceptional properties of event cameras, such as high dynamic range and no motion blur. However, feature asynchroni…
AttributeObjectobject-detectionObject DetectionHigh-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization
We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views while preserving specific details of the input image. High-fidelity 3D GAN inversion …
AttributeGenerative Adversarial NetworkNovel View SynthesisVocal Bursts Intensity Prediction