arXiv:2607.12176v2 Announce Type: replace
Abstract: Ambivalence and hesitancy (A/H) undermine digital behaviour-change interventions, and recognizing them automatically from video is the goal of the...
By Josep Cabacas-Maso, Ismael Benito-Altamirano, Carles Ventura
arXiv:2607.13345v2 Announce Type: replace
Abstract: We present a frame-independent audio-text system for the 3rd Ambivalence/Hesitancy Video Recognition Challenge at the 11th Affective & Behavior Ana...
By Luiz F. B. F. Martins, Rodrigo W. Pisaia, Matheus M. Girardi, Isabella V. Berkembrock, Jo\~ao A. Almeida, Andre G. Hochuli, Rayson Laroca, Alceu S. Britto Jr
arXiv:2607. 25961v1 Announce Type: cross Abstract: Ambivalence and hesitancy (A/H) are conflicting affective states that precede the delay or abandonment of health behaviour change.
By Podakanti Satyajith Chary, Barath Parthiban, Pranesh Velmurugan, Adeeba Khan, Nagarajan Ganapathy
The paper introduces the Modality Discrepancy Transformer (MDT), a model designed to detect ambivalence and hesitancy in clinical videos by capturing cross‑modal disagreement across facial, vocal, and linguistic signals. MDT expands a 6‑token representation to 9 tokens that include modality embeddings, absolute‑difference features, and Hadamard‑product discrepancy features, which are processed through Transformer self‑attention with FiLM‑based text conditioning and LoRA fine‑tuning. On the BAH dataset from the 3rd ABAW Challenge, MDT achieves a Macro F1 score of 0.7408 on the labelled test split and 0.7368 on the private leaderboard, surpassing the strongest baseline by over 10 points while training in under 20 minutes on a single GPU.
By Shiyu Luo, Yu Wang, Jiawen Huang, Zhaoxiang Xiao, Chenxi Huang, Qi Zhang, Bin Liu
arXiv:2607. 12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition.
By Aleksei Bakin, Andrey V. Savchenko
arXiv:2610.00825v1 Announce Type: new
Abstract: Dubbing quality control requires a reference-free judge that can determine whether a candidate text line matches a speaker's visible articulation in bo...
By Rui Liu, Bhavin Jawade, Haoqi Li, Shivam Mehta, Karan Saxena, Yinghong Lan, Cameron R. Wolfe