  Surgical phase recognition is crucial for enhancing the efficiency and safety
of computer-assisted interventions. One of the fundamental challenges involves
modeling the long-distance temporal relationships present in surgical videos.
Inspired by the recent success of Mamba, a state space model with linear
scalability in sequence length, this paper presents SR-Mamba, a novel
attention-free model specifically tailored to meet the challenges of surgical
phase recognition. In SR-Mamba, we leverage a bidirectional Mamba decoder to
effectively model the temporal context in overlong sequences. Moreover, the
efficient optimization of the proposed Mamba decoder facilitates single-step
neural network training, eliminating the need for separate training steps as in
previous works. This single-step training approach not only simplifies the
training process but also ensures higher accuracy, even with a lighter spatial
feature extractor. Our SR-Mamba establishes a new benchmark in surgical video
analysis by demonstrating state-of-the-art performance on the Cholec80 and
CATARACTS Challenge datasets. The code is accessible at
https://github.com/rcao-hk/SR-Mamba.
