From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding

Yong-Lu Li, Xiaoqian Wu, Xinpeng Liu, Zehao Wang, Yiming Dou, Yikun Ji, Junyi Zhang, Yixing Li, Xudong Lu, Jingru Tan, Cewu Lu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 16582-16592

Action understanding matters for intelligent agents and has attracted long-term attention. It can be formed as the mapping from the action physical space to the semantic space. Typically researchers built action datasets according to idiosyncratic choices to define classes and push the envelope of benchmarks respectively. Thus datasets are incompatible with each other like "Isolated Islands" due to semantic gaps and various class granularities e.g. do housework in dataset A and wash plate in dataset B. We argue that a more principled semantic space is an urgent need to concentrate the community efforts and enable us to use all datasets together to pursue generalizable action learning. To this end we design a structured action semantic space in view of verb taxonomy hierarchy and covering massive actions. By aligning the classes of previous datasets to our semantic space we gather (image/video/skeleton/MoCap) datasets into a unified database in a unified label system i.e. bridging "isolated islands" into a "Pangea". Accordingly we propose a novel model mapping from the physical space to semantic space to fully use Pangea. In extensive experiments our new system shows significant superiority especially in transfer learning. Our code and data will be made public at https://mvig-rhos.com/pangea.