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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.
Dual Compensation Residual Networks for Class Imbalanced Learning
Learning generalizable representation and classifier for class-imbalanced data is challenging for data-driven deep models.
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.
BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification
Detail Branch processes frames at original resolution to preserve the detailed visual clues, and Context Branch with a down-sampling strategy is employed to capture long-range contexts.
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.
Temporal Complementary Learning for Video Person Re-Identification
This paper proposes a Temporal Complementary Learning Network that extracts complementary features of consecutive video frames for video person re-identification.
Cross Attention Network for Few-shot Classification
The unseen classes and low-data problem make few-shot classification very challenging.
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.
