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CF-Net uses multimodal fusion for ambivalence and hesitancy recognition

By PulseAugur Editorial · [2 sources] ·

Researchers have developed CF-Net, a deep multimodal network designed to recognize ambivalence and hesitancy in videos. This network utilizes frozen SigLIP2, HuBERT, and DistilBERT backbones to process visual, audio, and transcript data. CF-Net incorporates a ConflictFusion module to compute cross-modal incongruence and speaker normalization to reduce identity leakage. The model achieved strong performance on the BAH dataset, reaching a Macro F1 score of 0.7364 on the private challenge test set. AI

IMPACT This research advances multimodal AI capabilities in understanding subtle human expressions like ambivalence and hesitancy.

RANK_REASON The cluster contains an academic paper detailing a new model and its performance on a specific challenge.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

CF-Net uses multimodal fusion for ambivalence and hesitancy recognition

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Tung Hung Bui, Hong Hai Nguyen, Van Thong Huynh ·

    CF-Net: Conflict Fusion with Speaker Normalisation and Certainty Weighting for Ambivalence/Hesitancy Recognition

    arXiv:2607.13976v1 Announce Type: new Abstract: Detecting ambivalence and hesitancy (AH) in unconstrained video is challenging because the target signal is inherently ambiguous and expressed through subtle cross-modal incongruence rather than prototypical affect. We present CF-Ne…

  2. arXiv cs.CV TIER_1 English(EN) · Van Thong Huynh ·

    CF-Net: Conflict Fusion with Speaker Normalisation and Certainty Weighting for Ambivalence/Hesitancy Recognition

    Detecting ambivalence and hesitancy (AH) in unconstrained video is challenging because the target signal is inherently ambiguous and expressed through subtle cross-modal incongruence rather than prototypical affect. We present CF-Net, a deep multimodal network submitted to the 3r…

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