SVF-CR Synchronized Visual-Facial Cross-Refinement for Multimodal Ambivalence and Hesitancy Recognition

Jul 16, 2026

Ambivalence and hesitancy are subtle behavioral states manifested through coordinated verbal content, facial behavior, visual context, and acoustic cues. Accurate recognition requires modeling how temporally aligned evidence interacts across modalities, not only learning unimodal representations. The paper introduces a synchronized visual-facial cross-refinement (SVF-CR) framework that extracts temporally partitioned whole-video and cropped-face tokens, refines them with self-attention and bidirectional cross-attention, builds segment-level visual-facial evidence via consistency/discrepancy modeling, and fuses enhanced multimodal cues for final decisions. Experiments on the BAH benchmark report improved public macro-F1.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

Introduction

Problem: Ambivalence and hesitancy recognition

Challenge: Weak and temporally distributed cues

Approach: Synchronized visual-facial cross-refinement

arXiv :2607 .09417v1 [ cs .CV] 10 Jul 2026 SVF-CR: Synchronized Visual-Facial Cross-Refinement for Multimodal Ambivalence and Hesitancy Recognition Hyein Park1 Namho Kim2 Junhwa Kim 1 ,∗ 1Dept. of AI Software Convergence, Konyang University, 2 Korean Broadcasting System (KBS) [26810503@konyang.ac.kr](26810503@konyang.ac.kr) , [namho96@kbs.co.kr](namho96@kbs.co.kr) , [junhwakim@konyang.ac.kr](junhwakim@konyang.ac.kr) ∗ Corresponding author 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 informative unimodal representations, but also modeling how temporally aligned behavioral evidence interacts across modalities. In this paper, we propose a synchronized visual-facial cross-refinement framework (SVF-CR) with pairwise multimodal evidence fusion for ambivalence and hesitancy recognition. The proposed method first extracts whole-video segment tokens and cropped-face segment tokens using the same temporal partition. The synchronized visual and facial tokens are refined through intra-modal self-attention and bidirectional visual-facial cross-attention, allowing wholevideo context and local facial behavior to mutually refine each other before evidence construction. We then construct segment-level visual-facial evidence using consistency and discrepancy modeling, followed by temporal self-attention and attention pooling. Textual and acoustic features are lightly refined through context self-attention and are fused with the enhanced visual-facial evidence at the final decision stage using pairwise evidence fusion. Experiments on the BAH (Behavioral Ambivalence/Hesitancy) public evaluation split show that the proposed synchronized visual-facial cross-refinement improves public macro-F1 over both global visual-face token fusion and synchronized evidence baselines, achieving a public macro-F1 of 0.7156. Code is available at : [https://github.com/hiinnnii/BAH-Challenge](https://github.com/hiinnnii/BAH-Challenge)ECCV2026 SVF-CR. 1 Introduction Understanding human ambivalence and hesitancy is important in many real-world interaction scenarios, including healthcare counseling, behavioral intervention, education, customer interviews, and human–computer interaction [1, 2] . In such settings, a person may not explicitly state uncertainty or reluctance, but may still reveal it through subtle verbal and non-verbal behaviors. Automatically recognizing these states can help interactive systems identify when a user is uncertain, resistant, or not fully committed, and can support more adaptive feedback, decision support, or intervention strategies [3] . Therefore, ambivalence and hesitancy recognition is not merely a binary classification problem, but a step toward understanding complex human intention and readiness for change in natural interactions [4] . Ambivalence and hesitancy are challenging because they are often expressed through weak, indirect, and temporally distributed cues. Unlike conventional emotion categories, these states are not always conveyed by a single clear facial expression or a specific spoken phrase [5, 6] . A participant may verbally provide an answer while showing uncertainty through facial movements, gaze changes, pauses, prosodic variation, or inconsistent non-verbal behavior [3, 7, 8] . In other cases, the verbal content may appear neutral, while facial and visual behavior reveals hesitation. This makes ambivalence and hesitancy recognition highly dependent on the relationship among textual, visual, facial, and acoustic evidence rather than on any single modality alone. A common strategy in affective behavior analysis is to combine multiple modalities, such as text, visual, and audio features [9, 10] . However, simple concatenation of heterogeneous modality features does not necessarily lead to better recognition. In t

FAQs

What is the main goal of the SVF-CR framework?

To recognize ambivalence and hesitancy by synchronizing visual (whole-video) and facial (cropped-face) evidence across modalities and modeling their temporal interactions.

How does SVF-CR align evidence across visual and facial streams?

It divides the video into temporal segments, extracts whole-video segment tokens and cropped-face segment tokens using the same partition, and compares them at the segment level.

What mechanisms are used to refine and combine multimodal information?

SVF-CR applies intra-modal self-attention and bidirectional visual-facial cross-attention to mutually refine tokens, then constructs segment-level evidence with consistency/discrepancy modeling, and fuses enhanced visual-facial evidence with lightly refined textual and acoustic features using pairwise evidence fusion.

Jul 16, 2026

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