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MCFL: multi-label contrastive focal loss for deep imbalanced pedestrian attribute recognition
Chen, Lin1; Song, Jingkuan2; Zhang, Xuerui1; Shang, Mingsheng1
2022-06-04
摘要Pedestrian Attribute Recognition (PAR) can provide valuable clues for several innovative surveillance applications. It is also a difficult task because inference of the multiple attributes at a far distance is challenging in real complex scenarios. Most existing methods improve the PAR with visual attention mechanisms or body-part detection modules, which increase the complexity of networks and require manual annotations of the human body. Also, uneven data distribution, leading to a decline in recall values, is still underestimated. This paper presents a novel multi-label optimization algorithm to mitigate these issues, named Multi-label Contrastive Focal Loss (MCFL). Specifically, we first propose a multi-label focal loss to emphasize the error-prone and minority attributes with a separated re-weighting scheme. And then, we introduce a multi-label contrastive learning strategy based on the multi-label divergences to help the deep network to distinguish the hard fine-grained attributes. We conduct extensive experiments on seven PAR benchmarks, and results indicate that the proposed MCFL with the native ResNet-50 backbone surpasses the state-of-the-art comparison methods in mean accuracy and recall.
关键词Pedestrian attribute recognition Multi-label contrastive loss Deep convolutional neural network Multi-label learning Imbalanced learning
DOI10.1007/s00521-022-07300-7
发表期刊NEURAL COMPUTING & APPLICATIONS
ISSN0941-0643
页码15
通讯作者Shang, Mingsheng(msshang@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000805925400004
语种英语