ReID-R achieves competitive person re-identification performance using chain-of-thought reasoning and reinforcement learning with only 14.3K non-trivial samples, about 20.9% of typical data scales, while providing interpretations.
Person Re-identification Meets Image Search
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
For long time, person re-identification and image search are two separately studied tasks. However, for person re-identification, the effectiveness of local features and the "query-search" mode make it well posed for image search techniques. In the light of recent advances in image search, this paper proposes to treat person re-identification as an image search problem. Specifically, this paper claims two major contributions. 1) By designing an unsupervised Bag-of-Words representation, we are devoted to bridging the gap between the two tasks by integrating techniques from image search in person re-identification. We show that our system sets up an effective yet efficient baseline that is amenable to further supervised/unsupervised improvements. 2) We contribute a new high quality dataset which uses DPM detector and includes a number of distractor images. Our dataset reaches closer to realistic settings, and new perspectives are provided. Compared with approaches that rely on feature-feature match, our method is faster by over two orders of magnitude. Moreover, on three datasets, we report competitive results compared with the state-of-the-art methods.
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
DPL-ReID adds dual prompt learning, real-world occlusion augmentation, and weighted gated fusion to CLIP for state-of-the-art occluded person re-identification on benchmark datasets.
citing papers explorer
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Thinking Before Matching: A Reinforcement Reasoning Paradigm Towards General Person Re-Identification
ReID-R achieves competitive person re-identification performance using chain-of-thought reasoning and reinforcement learning with only 14.3K non-trivial samples, about 20.9% of typical data scales, while providing interpretations.
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Dual-Prompt CLIP with Hybrid Visual Encoders for Occluded Person Re-Identification
DPL-ReID adds dual prompt learning, real-world occlusion augmentation, and weighted gated fusion to CLIP for state-of-the-art occluded person re-identification on benchmark datasets.