Video Mobile-Former: Video Recognition with Efficient Global Spatial-temporal Modeling

• URL: http://arxiv.org/abs/2208.12257v1
• Date: Thu, 25 Aug 2022 17:59:00 GMT
• Title: Video Mobile-Former: Video Recognition with Efficient Global Spatial-temporal Modeling
• Authors: Rui Wang and Zuxuan Wu and Dongdong Chen and Yinpeng Chen and Xiyang Dai and Mengchen Liu and Luowei Zhou and Lu Yuan and Yu-Gang Jiang
• Abstract summary: Transformer-based models have achieved top performance on major video recognition benchmarks. Video Mobile-Former is the first Transformer-based video model which constrains the computational budget within 1G FLOPs.
• Score: 125.95527079960725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based models have achieved top performance on major video recognition benchmarks. Benefiting from the self-attention mechanism, these models show stronger ability of modeling long-range dependencies compared to CNN-based models. However, significant computation overheads, resulted from the quadratic complexity of self-attention on top of a tremendous number of tokens, limit the use of existing video transformers in applications with limited resources like mobile devices. In this paper, we extend Mobile-Former to Video Mobile-Former, which decouples the video architecture into a lightweight 3D-CNNs for local context modeling and a Transformer modules for global interaction modeling in a parallel fashion. To avoid significant computational cost incurred by computing self-attention between the large number of local patches in videos, we propose to use very few global tokens (e.g., 6) for a whole video in Transformers to exchange information with 3D-CNNs with a cross-attention mechanism. Through efficient global spatial-temporal modeling, Video Mobile-Former significantly improves the video recognition performance of alternative lightweight baselines, and outperforms other efficient CNN-based models at the low FLOP regime from 500M to 6G total FLOPs on various video recognition tasks. It is worth noting that Video Mobile-Former is the first Transformer-based video model which constrains the computational budget within 1G FLOPs.

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