Audio-Adaptive Activity Recognition Across Video Domains
This addresses domain shift in activity recognition for video analysis, offering a novel approach but is incremental as it builds on existing domain adaptation methods by incorporating audio.
The paper tackles activity recognition under domain shift by leveraging activity sounds for domain adaptation, proposing an audio-adaptive encoder and audio-infused recognizer to adjust visual features and model cross-modal interactions. Experiments on datasets including EPIC-Kitchens and CharadesEgo demonstrate its effectiveness.
This paper strives for activity recognition under domain shift, for example caused by change of scenery or camera viewpoint. The leading approaches reduce the shift in activity appearance by adversarial training and self-supervised learning. Different from these vision-focused works we leverage activity sounds for domain adaptation as they have less variance across domains and can reliably indicate which activities are not happening. We propose an audio-adaptive encoder and associated learning methods that discriminatively adjust the visual feature representation as well as addressing shifts in the semantic distribution. To further eliminate domain-specific features and include domain-invariant activity sounds for recognition, an audio-infused recognizer is proposed, which effectively models the cross-modal interaction across domains. We also introduce the new task of actor shift, with a corresponding audio-visual dataset, to challenge our method with situations where the activity appearance changes dramatically. Experiments on this dataset, EPIC-Kitchens and CharadesEgo show the effectiveness of our approach.