Mitigating Context Bias in Action Recognition via Skeleton-Dominated Two-Stream Network
Qiankun Li, Xiaolong Huang, Yanshuang Luo, Xiaoyu Hu, Xinyu Sun, Zengfu Wang · 2023
In the realm of intelligent manufacturing and industrial upgrading, sophisticated multimedia computing technologies play a pivotal role in the recognition of video actions. However, most studies suffer from the issue of background bias, where the models excessively focus on the contextual information within the videos rather than concentrating on comprehending the human actions themselves. This could potentially lead to severe misjudgments in industrial applications. In this paper, we propose a Skeleton-Dominated Two-Stream Network (SDTSN), which is a novel two-stream framework that fuses and ensembles the skeleton and RGB modalities for video action recognition. Experimental results on the Mimetics dataset, without any background bias, demonstrate the efficacy of our approach.