Papers › Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation

Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation

1 Sep 2018ECCV 2018 9archive 2025-07-28

Xuecheng Nie, Jiashi Feng, Shuicheng Yan

This paper presents a novel Mutual Learning to Adapt model (MuLA) for joint human parsing and pose estimation. It effectively exploits mutual benefits from both tasks and simultaneously boosts their performance. Different from existing post-processing or multi-task learning based methods, MuLA predicts dynamic task-specific model parameters via recurrently leveraging guidance information from its parallel tasks. Thus MuLA can fast adapt parsing and pose models to provide more powerful representations by incorporating information from their counterparts, giving more robust and accurate results. MuLA is implemented with convolutional neural networks and end-to-end trainable. Comprehensive experiments on benchmarks LIP and extended PASCAL-Person-Part demonstrate the effectiveness of the proposed MuLA model with superior performance to well established baselines.

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