Generative Modeling for Multi-task Visual Learning

Zhipeng Bao; Martial Hebert; Yu-Xiong Wang

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arXiv · 2106.13409

Generative Modeling for Multi-task Visual Learning

Zhipeng Bao·Martial Hebert·Yu-Xiong Wang

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Abstract

Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images. In this paper, motivated by multi-task learning of shareable feature representations, we consider a novel problem of learning a shared generative model that is useful across various visual perception tasks. Correspondingly, we propose a general multi-task oriented generative modeling (MGM) framework, by coupling a discriminative multi-task network with a generative network. While it is challenging to synthesize both RGB images and pixel-level annotations in multi-task scenarios, our framework enables us to use synthesized images paired with only weak annotations (i.e., image-level scene labels) to facilitate multiple visual tasks. Experimental evaluation on challenging multi-task benchmarks, including NYUv2 and Taskonomy, demonstrates that our MGM framework improves the performance of all the tasks by large margins, consistently outperforming state-of-the-art multi-task approaches.

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BibTeX RIS

Zhipeng Bao, Martial Hebert, Yu-Xiong Wang. 2021-06-25. Generative Modeling for Multi-task Visual Learning. https://arxiv.org/abs/2106.13409

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