Can I read JoVALE: Detecting Human Actions in Video Using Audiovisual and Language Contexts on EtoBox?

JoVALE: Detecting Human Actions in Video Using Audiovisual and Language Contexts by Son, Taein; Seo, Soo Won; Kim, Jisong; Lee, Seok Hwan; Choi, Jun Won is a scholarly article available to read on EtoBox.

What is JoVALE: Detecting Human Actions in Video Using Audiovisual and Language Contexts about?

Video Action Detection (VAD) entails localizing and categorizing action instances within videos, which inherently consist of diverse information sources such as audio, visual cues, and surrounding scene contexts. Leveraging this multi-modal information effectively for VAD poses a significant challenge, as the model must identify action-relevant cues with precision. In this study, we introduce a novel multi-modal VAD architecture, referred to as the Joint Actor-centric Visual, Audio, Language Encoder (JoVALE). JoVALE is the first VAD method to integrate audio and visual features with scene descriptive context sourced from large-capacity image captioning models. At the heart of JoVALE is the actor-centric aggregation of audio, visual, and scene descriptive information, enabling adaptive integration of crucial features for recognizing each actor's actions. We have developed a Transformer-based architecture, the Actor-centric Multi-modal Fusion Network, specifically designed to capture the dynamic interactions among actors and their multi-modal contexts. Our evaluation on three prominent VAD benchmarks, including AVA, UCF101-24, and JHMDB51-21, demonstrates that incorporating multi-mod

Author

Son, Taein; Seo, Soo Won; Kim, Jisong; Lee, Seok Hwan; Choi, Jun Won

Published

2024

Language

EN