The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The paper proposes identity-conditioned score fusion for open-set person re-identification, a framework that assigns weights tailored to each gallery identity without requiring additional training. By comparing intra-identity consistency with cross-identity impostors, it derives identity-specific profiles that work with query-conditioned adaptation through a parameter-free rule, enhancing the separation between true and false matches while maintaining score calibration. Experiments on three clothes-changing benchmarks show consistent improvements over statistical, rank-based, and learned baselines, achieving up to an 8.8% absolute reduction in the false non-identification rate.
The paper introduces HiDeR, a High Information Density Replay framework for Lifelong Person Re-Identification that replaces discrete sample selection with information compression. It uses a complexity‑aware memory allocation based on intra‑class variance and a metric‑guided condensation objective to preserve essential identity topologies, while a cross‑modality adaptation strategy bridges synthetic and real styles to improve training. Experiments show HiDeR outperforms state‑of‑the‑art methods in knowledge retention and generalization, and reduces cumulative replay cost.
Robust person re-identification often combines complementary cues such as face, gait, and body shape. While adaptive fusion typically targets query quality, model strength also varies across identitie...
MINER is a model-agnostic framework designed to improve clothes-changing person re-identification by suppressing garment bias. It works by contrasting hard examples and adaptively balancing intra- and inter-person variance in the dataset. Experiments demonstrate that MINER achieves state-of-the-art performance on four CC-ReID benchmarks and ranks among the leaders in all three tracks of the open TVRID RGB-Depth ReID challenge.
arXiv:2606. 02242v1 Announce Type: cross Abstract: The joint optimization of image-based (I2I) and text-based (T2I) person re-identification (ReID) is hindered by modality discrepancies and conflicting training objectives, leading to suboptimal shared representations.
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...