Adaptive Recursive Circle Framework for Fine-grained Action Recognition

• URL: http://arxiv.org/abs/2107.11813v1
• Date: Sun, 25 Jul 2021 14:24:29 GMT
• Title: Adaptive Recursive Circle Framework for Fine-grained Action Recognition
• Authors: Hanxi Lin, Xinxiao Wu, Jiebo Luo
• Abstract summary: How to model fine-grained spatial-temporal dynamics in videos has been a challenging problem for action recognition. Most existing methods generate features of a layer in a pure feedforward manner. We propose an Adaptive Recursive Circle framework, a fine-grained decorator for pure feedforward layers.
• Score: 95.51097674917851
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How to model fine-grained spatial-temporal dynamics in videos has been a challenging problem for action recognition. It requires learning deep and rich features with superior distinctiveness for the subtle and abstract motions. Most existing methods generate features of a layer in a pure feedforward manner, where the information moves in one direction from inputs to outputs. And they rely on stacking more layers to obtain more powerful features, bringing extra non-negligible overheads. In this paper, we propose an Adaptive Recursive Circle (ARC) framework, a fine-grained decorator for pure feedforward layers. It inherits the operators and parameters of the original layer but is slightly different in the use of those operators and parameters. Specifically, the input of the layer is treated as an evolving state, and its update is alternated with the feature generation. At each recursive step, the input state is enriched by the previously generated features and the feature generation is made with the newly updated input state. We hope the ARC framework can facilitate fine-grained action recognition by introducing deeply refined features and multi-scale receptive fields at a low cost. Significant improvements over feedforward baselines are observed on several benchmarks. For example, an ARC-equipped TSM-ResNet18 outperforms TSM-ResNet50 with 48% fewer FLOPs and 52% model parameters on Something-Something V1 and Diving48.

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