JARViS: Detecting Actions in Video Using Unified Actor-Scene Context Relation Modeling

• URL: http://arxiv.org/abs/2408.03612v2
• Date: Tue, 17 Sep 2024 06:25:38 GMT
• Title: JARViS: Detecting Actions in Video Using Unified Actor-Scene Context Relation Modeling
• Authors: Seok Hwan Lee, Taein Son, Soo Won Seo, Jisong Kim, Jun Won Choi,
• Abstract summary: Two-stage Video localization (VAD) is a formidable task that involves the localization and classification of actions within the spatial and temporal dimensions of a video clip. We propose a two-stage VAD framework called Joint Actor-scene context Relation modeling (JARViS) JARViS consolidates cross-modal action semantics distributed globally across spatial and temporal dimensions using Transformer attention.
• Score: 8.463489896549161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video action detection (VAD) is a formidable vision task that involves the localization and classification of actions within the spatial and temporal dimensions of a video clip. Among the myriad VAD architectures, two-stage VAD methods utilize a pre-trained person detector to extract the region of interest features, subsequently employing these features for action detection. However, the performance of two-stage VAD methods has been limited as they depend solely on localized actor features to infer action semantics. In this study, we propose a new two-stage VAD framework called Joint Actor-scene context Relation modeling based on Visual Semantics (JARViS), which effectively consolidates cross-modal action semantics distributed globally across spatial and temporal dimensions using Transformer attention. JARViS employs a person detector to produce densely sampled actor features from a keyframe. Concurrently, it uses a video backbone to create spatio-temporal scene features from a video clip. Finally, the fine-grained interactions between actors and scenes are modeled through a Unified Action-Scene Context Transformer to directly output the final set of actions in parallel. Our experimental results demonstrate that JARViS outperforms existing methods by significant margins and achieves state-of-the-art performance on three popular VAD datasets, including AVA, UCF101-24, and JHMDB51-21.

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