Neural Wave Propagation for Surgical Video Action Recognition: A New Dataset and Baseline

Accurate and efficient recognition of surgical actions in videos is critical for advancing AI-driven surgical robotics. However, current surgical video action recognition (SVAR) datasets suffer from limitations such as small scale, low resolution, inconsistent annotations, and insufficient action coverage. Most latest video recognition models are trained on large-scale common datasets and underperform in SVAR due to architectures that suppress high-frequency visual details (crucial for recognizing surgical tools and motions) and lack a strong spatial inductive bias, requiring extensive training data for good convergence. This is particularly challenging in the surgical domain, where data access is limited. Therefore, a new baseline is required. To address these issues, we introduce LapSurg-230K, an SVAR dataset of 7,569 high-resolution laparoscopic surgical video clips with 230,246 frames, well-annotated for 11 key actions across 9 surgery types. It supports both full and progressive data volume evaluation settings. We further propose WaveR, an attention-free baseline based on physical wave propagation. WaveR embeds an innate physical inductive bias: each video patch acts as a wave source that propagates waves toward action-critical regions (e.g., instrument tips), adaptively aggregating spatial-temporal context while preserving high-frequency surgical cues. This mechanism eliminates dependency on massive training data. Experiments demonstrate WaveR's robustness under extreme data scarcity ( $\leq 30\%$ training samples), achieving state-of-the-art accuracy on both surgical video action recognition and phase recognition tasks. The complete dataset, licensed under CC-BY 4.0, is available at https://doi.org/10.6084/m9.figshare.32237319. Our code is available at https://github.com/yezizi1022/WaveR_TIP.