Simple Features and Honest Calibration for Ambivalence and Hesitancy Recognition in Video
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 linguistic hesitation cues, fused by a reliability gate we call Affective Marker Fusion (AMF), and finished with a simple AP-weighted ensemble at a fixed decision threshold. We also introduce \emph{ASR-erased time}: speech recognisers delete fillers and hesitation pauses from the transcript, but the chunk timestamps keep the time those events took, and sixteen features built from these gaps form the strongest and most in...
| Authors | Vikas Kumar, Aditya Mishra, Haroon R. Lone |
|---|---|
| Primary category | cs.CV |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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