Object-centric Video Representation for Long-term Action Anticipation
This work addresses the problem of predicting future human-object interactions in videos for applications like robotics and surveillance, presenting an incremental improvement by leveraging pretrained models without finetuning.
The paper tackled long-term action anticipation in videos by building object-centric representations using visual-language pretrained models with object prompts, achieving effectiveness confirmed through evaluations on benchmarks like Ego4D, 50Salads, and EGTEA Gaze+.
This paper focuses on building object-centric representations for long-term action anticipation in videos. Our key motivation is that objects provide important cues to recognize and predict human-object interactions, especially when the predictions are longer term, as an observed "background" object could be used by the human actor in the future. We observe that existing object-based video recognition frameworks either assume the existence of in-domain supervised object detectors or follow a fully weakly-supervised pipeline to infer object locations from action labels. We propose to build object-centric video representations by leveraging visual-language pretrained models. This is achieved by "object prompts", an approach to extract task-specific object-centric representations from general-purpose pretrained models without finetuning. To recognize and predict human-object interactions, we use a Transformer-based neural architecture which allows the "retrieval" of relevant objects for action anticipation at various time scales. We conduct extensive evaluations on the Ego4D, 50Salads, and EGTEA Gaze+ benchmarks. Both quantitative and qualitative results confirm the effectiveness of our proposed method.