Actor-Action Semantic Segmentation with Grouping Process Models

Abstract

Actor-action semantic segmentation made an important step toward advanced video understanding: what action is happening; who is performing the action; and where is the action happening in space-time. Current methods based on layered CRFs for this problem are local and unable to capture the long-ranging interactions of video parts. We propose a new model that combines the labeling CRF with a supervoxel hierarchy, where supervoxels at various scales provide cues for possible groupings of nodes in the CRF to encourage adaptive and long-ranging interactions. The new model defines a dynamic and continuous process of information exchange: the CRF influences what supervoxels in the hierarchy are active, and these active supervoxels, in turn, affect the connectivities in the CRF; we hence call it a grouping process model. By further incorporating the video-level recognition, the proposed method achieves a large margin of 60% relative improvement over the state of the art on the recent A2D large-scale video labeling dataset, which demonstrates the effectiveness of our modeling.