Abstract
Pedestrian attribute recognition (PAR) in surveillance is to predict pedestrian visual features (somatotype, wearing style, etc.). Existing methods usually elaborately design complex multi-label deep neural networks to solve it, which is hard to take advantage of attribute correlations and prone to suffering from the negative transfer problem. In this paper, we proposed a grouping and recurrent feature encoding based multi-task learning method to solve these problems. We group attributes adaptively based on attribute learning state and use Bi-direction recurrent neural network (Bi-RNN) to acquire the encodings of different groups to build a auxiliary learning task. We optimize group learning and feature encoding simultaneously in an end-to-end multi-task learning (MTL) manner. Furthermore, we establish dynamic loss module to enable the model learn the weight automatically for different tasks in a closed-loop way. Finally, after finishing training, the proposed method allow us to remove auxiliary module and merge all group into one to get a concise yet effective model without weakening the performance. Extensive experimental results in two public datasets, PA-100K and RAP has demonstrated the performance superiority of our method.
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Zheng, S., Tang, B., Pan, H., Zhang, X., Yin, J. (2020). Grouping and Recurrent Feature Encoding Based Multi-task Learning for Pedestrian Attribute Recognition. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_7
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