MINER: Modeling Inter- and Intra-Person Variance for Reidentification
We present MINER, a simple model-agnostic framework that suppresses garment bias by contrasting hard examples and adaptively balancing dataset-dependent intra- and inter-person variance.
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Key points
- Clothes-Changing Person Re-Identification (CC-ReID) remains challenging due to drastic appearance shifts across outfits.
- Most prior works devise biometric signal-extraction heuristics to enhance inter-person variance, e.g., using segmentation masks, while leaving intra-person variance uncontrolled.
- Extensive experiments show that MINER improves retrieval across CC-ReID benchmarks, achieving state-of-the-art performance on four datasets and ranking among the leaders in all three tracks of the open TVRID RGB-Depth ReID challenge.
Sources (1)
- [1]MINER: Modeling Inter- and Intra-Person Variance for ReidentificationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 09:34 PM
We present MINER, a simple model-agnostic framework that suppresses garment bias by contrasting hard examples and adaptively balancing dataset-dependent intra- and inter-person variance.
Clothes-Changing Person Re-Identification (CC-ReID) remains challenging due to drastic appearance shifts across outfits.
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