English(EN)SVF-CR: Synchronized Visual-Facial Cross-Refinement for Multimodal Ambivalence and Hesitancy Recognition
AI系统在视频分析中提升矛盾和犹豫识别能力 · 跟踪8个来源
作者PulseAugur 编辑部·[8 个来源]·
研究人员开发了识别视频中矛盾和犹豫的先进方法，参加了第11届ABAW挑战赛。其中一种方法，HSEmotion团队的系统，利用多任务学习，结合冻结的轻量级面部提取器和后处理技术来预测效价、唤醒度、面部表情和动作单元。另一个系统SVF-CR采用同步视觉-面部交叉精炼框架进行多模态证据融合。第三种方法侧重于简单特征和诚实校准，引入“ASR擦除时间”来捕捉犹豫停顿，并使用称为情感标记融合的可靠性门控。
AI
arXiv:2607.12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition. For multi-task learning with simultaneous prediction of valence, arousal, facial expressions, and action units on s-Aff-Wil…
arXiv cs.CL
TIER_1English(EN)·Vikas Kumar, Aditya Mishra, Haroon R. Lone·
arXiv:2607.11120v1 Announce Type: cross Abstract: We address ambivalence and hesitancy (A/H) recognition in the ABAW 2026 BAH Challenge: given a short interview video, predict whether the person shows signs of A/H. Our system combines affect-specialised text, audio, and visual re…
We address ambivalence and hesitancy (A/H) recognition in the ABAW 2026 BAH Challenge: given a short interview video, predict whether the person shows signs of A/H. Our system combines affect-specialised text, audio, and visual representations with a small set of readable linguis…
We address ambivalence and hesitancy (A/H) recognition in the ABAW 2026 BAH Challenge: given a short interview video, predict whether the person shows signs of A/H. Our system combines affect-specialised text, audio, and visual representations with a small set of readable linguis…
arXiv cs.AI
TIER_1English(EN)·Hyein Park, Namho Kim, Junhwa Kim·
arXiv:2607.09417v1 Announce Type: cross Abstract: Ambivalence and hesitancy are subtle behavioral states that are expressed through a combination of verbal content, facial behavior, visual context, and acoustic cues. Effective recognition therefore requires not only extracting in…
Ambivalence and hesitancy are subtle behavioral states that are expressed through a combination of verbal content, facial behavior, visual context, and acoustic cues. Effective recognition therefore requires not only extracting informative unimodal representations, but also model…
arXiv:2607.12176v1 Announce Type: new Abstract: Ambivalence and hesitancy (A/H) undermine digital behaviour-change interventions, and recognizing them automatically from video is the goal of the ABAW A/H challenge on the BAH dataset. We describe our system for the 11th edition of…
arXiv cs.CV
TIER_1English(EN)·Andrey V. Savchenko·
This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition. For multi-task learning with simultaneous prediction of valence, arousal, facial expressions, and action units on s-Aff-Wild2 dataset, we use frozen lightweight facial extra…
