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Found 9 papers, 6 papers with code
Clothes-Changing Person Re-Identification with Feasibility-Aware Intermediary Matching
Current clothes-changing person re-identification (re-id) approaches usually perform retrieval based on clothes-irrelevant features, while neglecting the potential of clothes-relevant features.
Clothes-Changing Person Re-identification with RGB Modality Only
In this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine clothes-irrelevant features from the original RGB images by penalizing the predictive power of re-id model w. r. t.
Ranked #1 on
Multiview Gait Recognition
on CASIA-B
Clothes Changing Person Re-Identification
Multiview Gait Recognition
Basket-based Softmax
Experimentally, we demonstrate the efficiency and superiority of the BBS on the tasks of face recognition and re-identification, with both simulated and real-world datasets.
Feature Completion for Occluded Person Re-Identification
Our method significantly outperforms existing methods on the occlusion datasets, while remains top even superior performance on holistic datasets.
IAUnet: Global Context-Aware Feature Learning for Person Re-Identification
Furthermore, a Channel IAU (CIAU) module is designed to model the semantic contextual interactions between channel features to enhance the feature representation, especially for small-scale visual cues and body parts.
Appearance-Preserving 3D Convolution for Video-based Person Re-identification
Due to the imperfect person detection results and posture changes, temporal appearance misalignment is unavoidable in video-based person re-identification (ReID).
Temporal Knowledge Propagation for Image-to-Video Person Re-identification
With back propagation, temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated.
Ranked #9 on
Person Re-Identification
on iLIDS-VID
Image-To-Video Person Re-Identification
Video-Based Person Re-Identification
Interaction-and-Aggregation Network for Person Re-identification
Person re-identification (reID) benefits greatly from deep convolutional neural networks (CNNs) which learn robust feature embeddings.
VRSTC: Occlusion-Free Video Person Re-Identification
For one thing, the spatial structure of a pedestrian frame can be used to predict the occluded body parts from the unoccluded body parts of this frame.
