Layout-induced Video Representation for Recognizing Agent-in-Place Actions

International Conference on Computer Vision (ICCV)

Publication date: October 29, 2019

Ruichi Yu, Hongcheng Wang, Ang Li, Jingxiao Zheng, Vlad Morariu, Larry S. Davis

We address scene layout modeling for recognizing agentin-place actions, which are actions associated with agents who perform them and the places where they occur, in the context of outdoor home surveillance. We introduce a novel representation to model the geometry and topology of scene layouts so that a network can generalize from the layouts observed in the training scenes to unseen scenes in the test set. This Layout-Induced Video Representation (LIVR) abstracts away low-level appearance variance and encodes geometric and topological relationships of places to explicitly model scene layout. LIVR partitions the semantic features of a scene into different places to force the network to learn generic place-based feature descriptions which are independent of specific scene layouts; then, LIVR dynamically aggregates features based on connectivities of places in each specific scene to model its layout. We introduce a new Agent-in Place Action (APA) dataset to show that our method allows neural network models to generalize significantly better to unseen scenes.

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