Multi-Scale Attention and Gated Shifting for Fine-Grained Event Spotting in Videos

Precise Event Spotting (PES) in sports videos requires frame-level recognition of fine-grained actions from single-camera footage. Existing PES models typically incorporate lightweight temporal modules such as Gate Shift Module (GSM) or Gate Shift Fuse (GSF) to enrich 2D CNN feature extractors with temporal context. However, these modules are limited in both temporal receptive field and spatial adaptability. We propose a Multi-Scale Attention Gate Shift Module (MSAGSM) that enhances GSM with multi-scale temporal dilations and multi-head spatial attention, enabling efficient modeling of both short- and long-term dependencies while focusing on salient regions. MSAGSM is a lightweight plug-and-play module that can be easily integrated with various 2D backbones. To further advance the field, we introduce the Table Tennis Australia (TTA) dataset-the first PES benchmark for table tennis-containing over 4800 precisely annotated events. Extensive experiments across five PES benchmarks demonstrate that MSAGSM consistently improves performance with minimal overhead, setting new state-of-the-art results.