  Video understanding requires the extraction of rich spatio-temporal
representations, which transformer models achieve through self-attention.
Unfortunately, self-attention poses a computational burden. In NLP, Mamba has
surfaced as an efficient alternative for transformers. However, Mamba's
successes do not trivially extend to computer vision tasks, including those in
video analysis. In this paper, we theoretically analyze the differences between
self-attention and Mamba. We identify two limitations in Mamba's token
processing: historical decay and element contradiction. We propose
VideoMambaPro (VMP) that solves the identified limitations by adding masked
backward computation and elemental residual connections to a VideoMamba
backbone. VideoMambaPro shows state-of-the-art video action recognition
performance compared to transformer models, and surpasses VideoMamba by clear
margins: 7.9% and 8.1% top-1 on Kinetics-400 and Something-Something V2,
respectively. Our VideoMambaPro-M model achieves 91.9% top-1 on Kinetics-400,
only 0.2% below InternVideo2-6B but with only 1.2% of its parameters. The
combination of high performance and efficiency makes VideoMambaPro an
interesting alternative for transformer models.
