PosMLP-Video: Spatial and Temporal Relative Position Encoding for Efficient Video Recognition

TL;DR

PosMLP-Video introduces an efficient MLP-based backbone for video recognition that uses relative positional encoding to model spatio-temporal relations, achieving competitive accuracy with fewer parameters and FLOPs.

Contribution

The paper proposes PosMLP-Video, a lightweight MLP-like model with novel relative positional encoding and gating units for efficient spatio-temporal video modeling.

Findings

Achieves 59.0% top-1 accuracy on Something-Something V1

Achieves 70.3% top-1 accuracy on Something-Something V2

Achieves 82.1% top-1 accuracy on Kinetics-400

Abstract

In recent years, vision Transformers and MLPs have demonstrated remarkable performance in image understanding tasks. However, their inherently dense computational operators, such as self-attention and token-mixing layers, pose significant challenges when applied to spatio-temporal video data. To address this gap, we propose PosMLP-Video, a lightweight yet powerful MLP-like backbone for video recognition. Instead of dense operators, we use efficient relative positional encoding (RPE) to build pairwise token relations, leveraging small-sized parameterized relative position biases to obtain each relation score. Specifically, to enable spatio-temporal modeling, we extend the image PosMLP's positional gating unit to temporal, spatial, and spatio-temporal variants, namely PoTGU, PoSGU, and PoSTGU, respectively. These gating units can be feasibly combined into three types of spatio-temporal factorized positional MLP blocks, which not only decrease model complexity but also maintain good performance. Additionally, we enrich relative positional relationships by using channel grouping. Experimental results on three video-related tasks demonstrate that PosMLP-Video achieves competitive speed-accuracy trade-offs compared to the previous state-of-the-art models. In particular, PosMLP-Video pre-trained on ImageNet1K achieves 59.0%/70.3% top-1 accuracy on Something-Something V1/V2 and 82.1% top-1 accuracy on Kinetics-400 while requiring much fewer parameters and FLOPs than other models. The code is released at https://github.com/zhouds1918/PosMLP_Video.