# HEDGE: A Calibrated Ensemble for A/H Recognition

arXiv: https://arxiv.org/abs/2607.12176 · HTML: https://mlsanity.com/paper/2607.12176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 39 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Builds on (most cited first, 29 of 29)

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- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
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- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
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- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification](https://mlsanity.com/paper/2108.10566/) (2021)
- [BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change](https://mlsanity.com/paper/2505.19328/) (2025)
- [From Affect to Complex Behavior: Advancing Multimodal Human-Centered AI at the 10th ABAW Workshop & Competition](https://mlsanity.com/paper/2605.27451/) (2026)
- [Subject-Based Domain Adaptation for Facial Expression Recognition](https://mlsanity.com/paper/2312.05632/) (2023)
- [Conflict-Aware Multimodal Fusion for Ambivalence and Hesitancy Recognition](https://mlsanity.com/paper/2603.15818/) (2026)
- [Enhancing Facial Expression Recognition through Dual-Direction Attention Mixed Feature Networks: Application to 7th ABAW Challenge](https://mlsanity.com/paper/2407.12390/) (2024)
- [Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method](https://mlsanity.com/paper/2508.09202/) (2025)
- [BROTHER: Behavioral Recognition Optimized Through Heterogeneous Ensemble Regularization for Ambivalence and Hesitancy](https://mlsanity.com/paper/2603.14361/) (2026)
- [Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos](https://mlsanity.com/paper/2603.21309/) (2026)
- [Team LEYA in 10th ABAW Competition: Multimodal Ambivalence/Hesitancy Recognition Approach](https://mlsanity.com/paper/2603.12848/) (2026)
- [Solution for 10th Competition on Ambivalence/Hesitancy (AH) Video Recognition Challenge using Divergence-Based Multimodal Fusion](https://mlsanity.com/paper/2603.16939/) (2026)
- [Nuanced Emotion Recognition Based on a Segment-based MLLM Framework Leveraging Qwen3-Omni for AH Detection](https://mlsanity.com/paper/2603.13406/) (2026)

## Abstract (arXiv)

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 HEDGE (Hesitancy/Ambivalence Estimation via Distribution-aware, Generalized Ensembling), 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. We also submitted four variants of this system to this year's private test (30 new participants): a fixed-threshold version, a single individual model, and an ensemble with cache-personalization test-time adaptation (TTA). The plain calibrated ensemble scored 0.7361 macro-F1 on the private test, closely matching our public-test estimate, and the TTA variant scored highest of all five at 0.7367 macro-F1, our official challenge result (team AIWELL, rank 5 of 12), even though TTA showed no benefit on the public test. The single individual model dropped to 0.6759, far more than any ensemble variant. We explain both results: TTA only has distribution shift to correct on the private test, which the public test lacks, and a text-only linear probe reaches 0.716 macro-F1 (within noise of the full system, correlated at 0.91 in its errors), so the ensemble's robustness to new participants comes from the same modality redundancy that makes single, less-diversified models comparatively brittle. We additionally report a systematic study of more than 60 further controlled experiments (modality, backbone, loss, and adaptation ablations) that did not improve on this system, and an explainability analysis showing the transcript's delivery style dominates the signal while the extractable non-verbal ceiling saturates near 0.60 macro-F1.