A Calibrated Multimodal Ensemble for Ambivalence/Hesitancy Recognition: System Description and Private-Test Submission Strategy

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 the challenge: a calibrated, equal-weight ensemble of three fusion models over frozen face, audio, text, and pose embeddings, which reaches 0.7358 macro-F1 on the public test set. This year's private test, released on a disjoint set of 30 new participants, is scored on five allowed submissions; we report the configuration and rationale of each of our five submissions, and, where already available, the private-test score obtained. Our first submission, an exact replica of the calibrated ensemble tuned only on public validation, scored 0.7361 macro-F1 on the private test, matching our public-test estimate almost exactly and confirming the pipeline generalizes to unseen participants without leakage.

Related

• Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions
• Calibrated Multimodal Representation Learning with Missing Modalities
• EmoMM: Benchmarking and Steering MLLM for Multimodal Emotion Recognition under Conflict and Missingness

Source: arXiv cs.CV | 2026-07-15