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- Title
MCFL: multi-label contrastive focal loss for deep imbalanced pedestrian attribute recognition.
- Authors
Chen, Lin; Song, Jingkuan; Zhang, Xuerui; Shang, Mingsheng
- Abstract
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.
- Subjects
PEDESTRIANS; ARTIFICIAL neural networks; CONVOLUTIONAL neural networks; HUMAN body; DATA distribution
- Publication
Neural Computing & Applications, 2022, Vol 34, Issue 19, p16701
- ISSN
0941-0643
- Publication type
Article
- DOI
10.1007/s00521-022-07300-7