Two-level iteration method for multi-task learning with task-isolated labels

Zhexiao Xiong; Xin Wen; Xu Zhao; Haiyun Guo; Chaoyang Zhao; Jinqiao Wang

doi:10.1117/12.2626861

2 March 2022 Two-level iteration method for multi-task learning with task-isolated labels

Zhexiao Xiong, Xin Wen, Xu Zhao, Haiyun Guo, Chaoyang Zhao, Jinqiao Wang

Proceedings Volume 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021); 1215813 (2022) https://doi.org/10.1117/12.2626861
Event: 2021 International Conference on Computer Vision and Pattern Analysis, 2021, Guangzhou, China

Abstract

Face attribute recognition plays a vital role in face-related tasks. Common face attributes include person age, person gender, mask-wearing, glasses-wearing, etc. Using one network to predict all attributes can save many computation costs. However, these attributes can hardly be fully labelled on every image in the same dataset since the labelling costs and the requirement on the sample balance. In many cases, each of the datasets are labelled with a single attribute. With several such datasets, how to use one network to generate the multi-task predictions for all attributes is a problem. In this paper, we propose a two-level iteration training method for multi-task face attribute learning with task-isolated labels. The two-level iteration method includes a task-level inner iteration and the regular outer iteration. With this scheme, the network receives the gradients from all tasks after each inner iteration. After training, the network is able to predict all attributes. Experiments show the effectiveness of the method and the advantages of multi-task learning over single-task learning on network accuracy and efficiency, which demonstrate the broad applicability and effectiveness of the proposed approach.

Citation Download Citation

Zhexiao Xiong, Xin Wen, Xu Zhao, Haiyun Guo, Chaoyang Zhao, and Jinqiao Wang "Two-level iteration method for multi-task learning with task-isolated labels", Proc. SPIE 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021), 1215813 (2 March 2022); https://doi.org/10.1117/12.2626861

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KEYWORDS

Facial recognition systems

Data modeling

Neural networks

Detection and tracking algorithms

Image processing

Computer vision technology

Iterative methods

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