In dyadic interactions, humans communicate their intentions and state of mind using verbal and non-verbal cues, where multiple different facial reactions might be appropriate in response to a specific speaker behaviour. Then, how to develop a machine learning (ML) model that can automatically generate multiple appropriate, diverse, realistic and synchronised human facial reactions from an previously unseen speaker behaviour is a challenging task. Following the successful organisation of the first REACT challenge (REACT 2023), this edition of the challenge (REACT 2024) employs a subset used by the previous challenge, which contains segmented 30-secs dyadic interaction clips originally recorded as part of the NOXI and RECOLA datasets, encouraging participants to develop and benchmark Machine Learning (ML) models that can generate multiple appropriate facial reactions (including facial image sequences and their attributes) given an input conversational partner's stimulus under various dyadic video conference scenarios. This paper presents: (i) the guidelines of the REACT 2024 challenge; (ii) the dataset utilized in the challenge; and (iii) the performance of the baseline systems on the two proposed sub-challenges: Offline Multiple Appropriate Facial Reaction Generation and Online Multiple Appropriate Facial Reaction Generation, respectively. The challenge baseline code is publicly available at https://github.com/reactmultimodalchallenge/baseline_react2024.
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React.js(React)是 Facebook 推出的一个用来构建用户界面的 JavaScript 库。
Facebook开源了React,这是该公司用于构建反应式图形界面的JavaScript库,已经应用于构建Instagram网站及 Facebook部分网站。最近出现了AngularJS、MeteorJS 和Polymer中实现的Model-Driven Views等框架,React也顺应了这种趋势。React基于在数据模型之上声明式指定用户界面的理念,用户界面会自动与底层数据保持同步。与前面提及 的框架不同,出于灵活性考虑,React使用JavaScript来构建用户界面,没有选择HTML。Not Rest