The paper introduces the CMCC-ReID task, constructs the SYSU-CMCC benchmark dataset, and proposes the PIA network with disentangling and prototype modules that outperforms prior methods on combined modality and clothing variations.
arXiv preprint arXiv:2005.04966 (2020)
4 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
verdicts
UNVERDICTED 4roles
method 1polarities
use method 1representative citing papers
Adding register tokens to Vision Transformers eliminates high-norm background artifacts and raises state-of-the-art performance on dense visual prediction tasks.
SRENet introduces spectral decomposition blocks, re-entry blocks, and frequency-aware contrastive learning to achieve state-of-the-art results on point cloud action recognition benchmarks.
Dynamics-centric mixtures of local reconstruction, temporal continuity, and in-context dynamics objectives in PathoFM yield the most balanced transfer across tasks and subjects on clinical gait time series.
citing papers explorer
-
CMCC-ReID: Cross-Modality Clothing-Change Person Re-Identification
The paper introduces the CMCC-ReID task, constructs the SYSU-CMCC benchmark dataset, and proposes the PIA network with disentangling and prototype modules that outperforms prior methods on combined modality and clothing variations.