Abstract
Most of learning targets for multi-person pose estimation are based on the likelihood \(P(Y|X)\). However, if we construct the causal assumption for keypoints, named a Structure Causal Model (SCM) for the causality, \(P(Y|X)\) will introduce the bias via spurious correlations in the SCM. In practice, it appears as that networks may make biased decisions in the dense area of keypoints. Therefore, we propose a novel learning method, named Causal Intervention pose Network (CIposeNet). Causal intervention is a learning method towards solving bias in the SCM of keypoints. Specifically, under the consideration of causal inference, CIposeNet is developed based on the backdoor adjustment and the learning target will change into causal intervention \(P(Y|do(X))\) instead of the likelihood \(P(Y|X)\). The experiments conducted on multi-person datasets show that CIposeNet indeed releases bias in the networks.
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Yue, L., Li, J., Liu, Q. (2022). Causal Intervention Learning for Multi-person Pose Estimation. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13188. Springer, Cham. https://doi.org/10.1007/978-3-031-02375-0_14
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