Action-Region Mixing: A Video Augmentation Approach for Reducing Scene Bias in Action Recognition

Video action recognition models can rely heavily on recurring scene context rather than action-relevant cues, limiting their generalization to background changes. foreground–background mixing has therefore emerged as a useful augmentation strategy for reducing such scene dependence. However, existing semantic mixing approaches typically rely on off-the-shelf object detectors and associated preprocessing and postprocessing pipelines to localize action-related regions, while bounding-box-based representations may introduce unrelated background content into the mixed samples. To address these limitations, we propose ActionRegionMix, a prompt-guided, action-aware video augmentation framework that leverages a mask-generating Multimodal Large Language Model (MLLM) to extract action-related regions at the pixel level in an open-vocabulary manner. The extracted action regions are transferred across videos and composited with backgrounds from other training samples, introducing contextual variation while preserving action-relevant content. Experiments on UCF101 and HMDB51 show that ActionRegionMix consistently improves recognition performance over the no-augmentation baseline and the compared bounding-box-based semantic augmentation methods. These results demonstrate the effectiveness of pixel-level action-region mixing as a video augmentation strategy, offering a promising approach to enhance background diversity and mitigate reliance on scene-specific cues.