Actor-Action Semantic Segmentation with Grouping Process Models

Chenliang Xu; Jason J. Corso

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arXiv · 1512.09041

Actor-Action Semantic Segmentation with Grouping Process Models

Chenliang Xu·Jason J. Corso

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Abstract

Actor-action semantic segmentation made an important step toward advanced video understanding problems: what action is happening; who is performing the action; and where is the action in space-time. Current models for this problem are local, based on layered CRFs, and are unable to capture long-ranging interaction of video parts. We propose a new model that combines these local labeling CRFs with a hierarchical supervoxel decomposition. The supervoxels provide cues for possible groupings of nodes, at various scales, in the CRFs to encourage adaptive, high-order groups for more effective labeling. Our model is dynamic and continuously exchanges information during inference: the local CRFs influence what supervoxels in the hierarchy are active, and these active nodes influence the connectivity in the CRF; we hence call it a grouping process model. The experimental results on a recent large-scale video dataset show a large margin of 60% relative improvement over the state of the art, which demonstrates the effectiveness of the dynamic, bidirectional flow between labeling and grouping.

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Chenliang Xu, Jason J. Corso. 2015-12-30. Actor-Action Semantic Segmentation with Grouping Process Models. https://arxiv.org/abs/1512.09041

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