DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition

TL;DR

DualFormer is a novel transformer architecture that efficiently captures local and global spatiotemporal dependencies in video recognition, significantly reducing computational costs while maintaining high accuracy.

Contribution

It introduces a dual-level stratification of space-time attention, combining local and global dependencies, which improves efficiency and effectiveness over existing methods.

Findings

Achieves 82.9% top-1 accuracy on Kinetics-400 with ~1000G FLOPs

Outperforms existing methods with at least 3.2x fewer FLOPs

Verifies superior performance on five video benchmarks

Abstract

While transformers have shown great potential on video recognition with their strong capability of capturing long-range dependencies, they often suffer high computational costs induced by the self-attention to the huge number of 3D tokens. In this paper, we present a new transformer architecture termed DualFormer, which can efficiently perform space-time attention for video recognition. Concretely, DualFormer stratifies the full space-time attention into dual cascaded levels, i.e., to first learn fine-grained local interactions among nearby 3D tokens, and then to capture coarse-grained global dependencies between the query token and global pyramid contexts. Different from existing methods that apply space-time factorization or restrict attention computations within local windows for improving efficiency, our local-global stratification strategy can well capture both short- and long-range spatiotemporal dependencies, and meanwhile greatly reduces the number of keys and values in attention computation to boost efficiency. Experimental results verify the superiority of DualFormer on five video benchmarks against existing methods. In particular, DualFormer achieves 82.9%/85.2% top-1 accuracy on Kinetics-400/600 with ~1000G inference FLOPs which is at least 3.2x fewer than existing methods with similar performance. We have released the source code at https://github.com/sail-sg/dualformer.