# BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change

arXiv: https://arxiv.org/abs/2505.19328 · HTML: https://mlsanity.com/paper/2505.19328/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 54 of its 117 references that are in the map.
- First posted on arXiv 25 May 2025.

## Builds on (most cited first, 30 of 54)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation](https://mlsanity.com/paper/2110.04202/) (2021)
- [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://mlsanity.com/paper/1905.00641/) (2019)
- [TransFER: Learning Relation-aware Facial Expression Representations with Transformers](https://mlsanity.com/paper/2108.11116/) (2021)
- [Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition](https://mlsanity.com/paper/2205.01782/) (2022)
- [SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild](https://mlsanity.com/paper/1901.02839/) (2019)
- [POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition](https://mlsanity.com/paper/2204.04083/) (2022)
- [Expression Snippet Transformer for Robust Video-based Facial Expression Recognition](https://mlsanity.com/paper/2109.08409/) (2021)
- [Towards Multimodal Emotional Support Conversation Systems](https://mlsanity.com/paper/2408.03650/) (2024)
- [Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution](https://mlsanity.com/paper/2004.09681/) (2020)
- [Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies](https://mlsanity.com/paper/2108.10152/) (2021)
- [Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets](https://mlsanity.com/paper/2007.04178/) (2020)
- [Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition](https://mlsanity.com/paper/2111.05222/) (2021)
- [Audio-Visual Fusion for Emotion Recognition in the Valence-Arousal Space Using Joint Cross-Attention](https://mlsanity.com/paper/2209.09068/) (2022)
- [Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition](https://mlsanity.com/paper/2307.06947/) (2023)
- [LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise](https://mlsanity.com/paper/2307.09023/) (2023)
- [SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis](https://mlsanity.com/paper/1808.02531/) (2018)
- [Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport](https://mlsanity.com/paper/2401.15489/) (2024)
- [Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion Recognition](https://mlsanity.com/paper/2403.13659/) (2024)

## Built on by (most cited first, 14 of 14)

- [Conflict-Aware Multimodal Fusion for Ambivalence and Hesitancy Recognition](https://mlsanity.com/paper/2603.15818/) (2026)
- [BROTHER: Behavioral Recognition Optimized Through Heterogeneous Ensemble Regularization for Ambivalence and Hesitancy](https://mlsanity.com/paper/2603.14361/) (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)
- [Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions](https://mlsanity.com/paper/2604.11730/) (2026)
- [HSEmotion Team at the 11th ABAW Challenge: Multi-Task Learning and Ambivalence/Hesitancy Video Recognition](https://mlsanity.com/paper/2607.12774/) (2026)
- [HEDGE: A Calibrated Ensemble for A/H Recognition](https://mlsanity.com/paper/2607.12176/) (2026)
- [Audio-Text Cross-Attention with Psycholinguistic Support Features for Ambivalence/Hesitancy Recognition](https://mlsanity.com/paper/2607.13345/) (2026)
- [CF-Net: Conflict Fusion with Speaker Normalisation and Certainty Weighting for Ambivalence/Hesitancy Recognition](https://mlsanity.com/paper/2607.13976/) (2026)
- [Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach](https://mlsanity.com/paper/2607.14702/) (2026)
- [Multimodal Ambivalence and Hesitancy Recognition via Cross-Attention and Gated Fusion](https://mlsanity.com/paper/2607.15779/) (2026)
- [Knowledge-Guided Multimodal Reasoning over Interacting Streams for Video-Level Ambivalence and Hesitancy Recognition](https://mlsanity.com/paper/2607.25961/) (2026)
- [CALM-AH: An ABAW11-Calibrated Multimodal Ensemble with Reliability-Gated Multi-Expert Consensus for Video-Level Ambivalence and Hesitancy Recognition](https://mlsanity.com/paper/2607.29310/) (2026)
- [Modality Discrepancy Transformer for Ambivalence and Hesitancy Recognition](https://mlsanity.com/paper/2609.19148/) (2026)

## Abstract (arXiv)

Ambivalence and hesitancy (A/H), closely related constructs, are the primary reasons why individuals delay, avoid, or abandon health behaviour changes. They are subtle and conflicting emotions that sets a person in a state between positive and negative orientations, or between acceptance and refusal to do something. They manifest as a discord in affect between multiple modalities or within a modality, such as facial and vocal expressions, and body language. Although experts can be trained to recognize A/H as done for in-person interactions, integrating them into digital health interventions is costly and less effective. Automatic A/H recognition is therefore critical for the personalization and cost-effectiveness of digital behaviour change interventions. However, no datasets currently exist for the design of machine learning models to recognize A/H. This paper introduces the Behavioural Ambivalence/Hesitancy (BAH) dataset collected for multimodal recognition of A/H in videos. It contains 1,427 videos with a total duration of 10.60 hours, captured from 300 participants across Canada, answering predefined questions to elicit A/H. It is intended to mirror real-world digital behaviour change interventions delivered online. BAH is annotated by three experts to provide timestamps that indicate where A/H occurs, and frame- and video-level annotations with A/H cues. Video transcripts, cropped and aligned faces, and participant metadata are also provided. Since A and H manifest similarly in practice, we provide a binary annotation indicating the presence or absence of A/H. Additionally, this paper includes benchmarking results using baseline models on BAH for frame- and video-level recognition, and different learning setups. The limited performance highlights the need for adapted multimodal and spatio-temporal models for A/H recognition. The data and code are publicly available.