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arxiv: 2604.11730 · v3 · submitted 2026-04-13 · 💻 cs.CV · cs.HC· cs.LG

Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions

Manuela Gonz\'alez-Gonz\'alez , Soufiane Belharbi , Muhammad Osama Zeeshan , Masoumeh Sharafi , Muhammad Haseeb Aslam , Lorenzo Sia , Nicolas Richet , Marco Pedersoli
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Alessandro Lameiras Koerich Simon L Bacon Eric Granger
This is my paper

Pith reviewed 2026-05-10 15:56 UTC · model grok-4.3

classification 💻 cs.CV cs.HCcs.LG
keywords ambivalence and hesitancy recognitionmultimodal video analysisdigital health interventionsaffective computingdeep learning fusionBAH datasetpersonalized behavior change
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The pith

Standard deep learning models show limited success recognizing ambivalence and hesitancy in videos, indicating that better methods for handling multimodal conflicts are needed.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper explores automatic recognition of ambivalence and hesitancy, which are subtle conflicting emotions that can delay or prevent people from adopting healthy behaviors in digital health interventions. These emotions appear as inconsistencies in facial expressions, voice, body language, or spoken words. The authors test three setups on the BAH video dataset: ordinary supervised training, unsupervised domain adaptation to personalize the model to new users, and zero-shot inference with large language models. Performance across all setups remains low, which leads them to conclude that current video architectures cannot reliably capture the required spatio-temporal patterns or cross-modal conflicts.

Core claim

Applying standard deep learning pipelines to multimodal video for ambivalence and hesitancy recognition produces only limited accuracy, demonstrating that existing architectures are insufficient to exploit affective inconsistencies within and across modalities and that specialized spatio-temporal and multimodal fusion techniques will be required before such recognition can support personalized digital health interventions.

What carries the argument

Multimodal video analysis pipelines that combine facial, vocal, linguistic, and body cues to detect affective inconsistency, evaluated in supervised, domain-adaptation, and large-language-model zero-shot regimes on the BAH dataset.

If this is right

  • Improved fusion methods would allow digital health systems to detect when a user is wavering between acceptance and refusal of a recommended behavior.
  • Such detection would enable real-time personalization of interventions, for example by adjusting message framing or timing when ambivalence is flagged.
  • Domain adaptation and zero-shot LLM routes both inherit the same fusion shortcomings, so gains would require changes to the underlying video representation rather than only the training regime.
  • Accurate A/H recognition could reduce the cost and improve the scalability of behavior-change support in settings where in-person experts are unavailable.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar fusion limitations may appear in other video tasks that rely on detecting internal contradictions, such as multimodal deception detection or conflicting sentiment in conversation.
  • If better models are built, they could be tested for transfer to related affective states like uncertainty or mixed emotions in clinical or educational video data.
  • The finding suggests that progress on this task may depend more on new architectural primitives for inconsistency modeling than on simply scaling data or model size.

Load-bearing premise

That off-the-shelf deep learning video models can detect subtle emotional conflicts across and within modalities without new architectural adaptations for spatio-temporal fusion.

What would settle it

A new model that adds explicit spatio-temporal and cross-modal fusion layers and then achieves substantially higher accuracy on the same BAH test videos would show that the current limited performance is not an inherent limit of the task.

Figures

Figures reproduced from arXiv: 2604.11730 by Alessandro Lameiras Koerich, Eric Granger, Lorenzo Sia, Manuela Gonz\'alez-Gonz\'alez, Marco Pedersoli, Masoumeh Sharafi, Muhammad Haseeb Aslam, Muhammad Osama Zeeshan, Nicolas Richet, Simon L Bacon, Soufiane Belharbi.

Figure 1
Figure 1. Figure 1: Conceptual illustration of the theoretical pathway [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: BAH examples of frames with (green) and without (orange) A/H with cues detailed in [22]. impact of individual modalities/multimodal/fusion, temporal￾modeling/context. Video-level classification is also consid￾ered. 4.1 Pre-processing of Modalities Visual. Frames from each video captured at 24 fps are ex￾tracted, and for each frame, faces are cropped and aligned using the RetinaFace model [14]. The face wit… view at source ↗
Figure 3
Figure 3. Figure 3: Multimodal model used for baseline evaluation [ [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original abstract

Using behavioural science, health interventions focus on behaviour change by providing a framework to help patients acquire and maintain healthy habits that improve medical outcomes. In-person interventions are costly and difficult to scale, especially in resource-limited regions. Digital health interventions offer a cost-effective approach, potentially supporting independent living and self-management. Automating such interventions, especially through machine learning, has gained considerable attention recently. Ambivalence and hesitancy (A/H) play a primary role for individuals to delay, avoid, or abandon health interventions. A/H are subtle and conflicting emotions that place a person in a state between positive and negative evaluations of a behaviour, or between acceptance and refusal to engage in it. They manifest as affective inconsistency across modalities or within a modality, such as language, facial, vocal expressions, and body language. While experts can be trained to recognize A/H, 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 health interventions. Here, we explore the application of deep learning models for A/H recognition in videos, a multi-modal task by nature. In particular, this paper covers three learning setups: supervised learning, unsupervised domain adaptation for personalization, and zero-shot inference via large language models (LLMs). Our experiments are conducted on the unique and recently published BAH video dataset for A/H recognition. Our results show limited performance, suggesting that more adapted multi-modal models are required for accurate A/H recognition. Better methods for modeling spatio-temporal and multimodal fusion are necessary to leverage conflicts within/across modalities.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript explores the application of deep learning to multimodal ambivalence/hesitancy (A/H) recognition in videos from the BAH dataset. It evaluates three setups—supervised learning, unsupervised domain adaptation for personalization, and LLM zero-shot inference—and reports limited performance, concluding that more adapted models are needed for spatio-temporal and cross-modal fusion to capture affective inconsistencies.

Significance. If the reported limited performance is substantiated with quantitative evidence, the work usefully identifies open challenges in affective computing for digital health interventions, particularly the difficulty of modeling subtle conflicts within and across modalities. The inclusion of multiple learning paradigms (supervised, domain adaptation, zero-shot) is a positive aspect that broadens the empirical scope.

major comments (2)
  1. [Abstract] Abstract: The central claim that 'our results show limited performance' is load-bearing for the recommendation of better spatio-temporal and multimodal fusion methods, yet the abstract supplies no accuracy, F1, or other quantitative metrics, no baseline comparisons, no dataset statistics (e.g., number of videos, class balance), and no error bars. Without these, the claim that standard architectures are insufficient cannot be evaluated.
  2. [Experiments] Results/Experiments section (inferred from the three learning setups described): The manuscript states that standard deep learning architectures yield limited performance on A/H recognition but provides no details on the specific video models used (e.g., which spatio-temporal backbones, fusion strategies, or loss functions), making it impossible to assess whether the 'limited performance' stems from architectural limitations or from implementation choices.
minor comments (2)
  1. [Abstract] The abstract and introduction use 'A/H' without an initial definition on first use; expand the acronym at first mention for clarity.
  2. [Introduction] The manuscript refers to the 'unique and recently published BAH video dataset' but does not cite its source or provide a reference; add the appropriate citation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which helps strengthen the clarity and substantiation of our claims about the challenges in multimodal A/H recognition. We address each major comment below and will incorporate revisions to improve the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'our results show limited performance' is load-bearing for the recommendation of better spatio-temporal and multimodal fusion methods, yet the abstract supplies no accuracy, F1, or other quantitative metrics, no baseline comparisons, no dataset statistics (e.g., number of videos, class balance), and no error bars. Without these, the claim that standard architectures are insufficient cannot be evaluated.

    Authors: We agree that the abstract should be more self-contained to support the central claim. In the revised version, we will expand the abstract to report key quantitative results (accuracy, F1, and other relevant metrics for the supervised, domain adaptation, and zero-shot setups), include BAH dataset statistics (number of videos, class balance), reference baseline comparisons, and note error bars or variability from our runs. This will allow readers to directly evaluate the evidence for needing improved spatio-temporal and cross-modal fusion methods. revision: yes

  2. Referee: [Experiments] Results/Experiments section (inferred from the three learning setups described): The manuscript states that standard deep learning architectures yield limited performance on A/H recognition but provides no details on the specific video models used (e.g., which spatio-temporal backbones, fusion strategies, or loss functions), making it impossible to assess whether the 'limited performance' stems from architectural limitations or from implementation choices.

    Authors: We acknowledge the need for greater specificity in the experimental description. We will revise the Experiments section to explicitly detail the spatio-temporal backbones (e.g., the video encoders used for visual features), cross-modal fusion strategies (e.g., late fusion, attention mechanisms, or other approaches), and loss functions applied in each of the three learning setups. These additions will enable assessment of whether the observed limited performance arises from the task's inherent difficulties (affective inconsistencies across modalities) or from the particular implementation choices, thereby strengthening the motivation for more adapted models. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is a purely empirical application paper that applies off-the-shelf supervised video models, domain-adaptation techniques, and LLM zero-shot inference to the BAH dataset and reports the resulting performance numbers. No derivations, equations, parameter fittings, or self-referential definitions appear in the work; the modest conclusion that current architectures yield limited performance and that better spatio-temporal fusion is needed follows directly from the tabulated experimental outcomes without any reduction to the paper's own inputs or prior self-citations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract supplies no explicit free parameters, invented entities, or detailed axioms beyond standard machine-learning assumptions for video processing.

axioms (1)
  • domain assumption Deep learning models can learn representations of subtle emotional states from multimodal video data when trained on sufficient examples.
    Implicit premise required to justify applying off-the-shelf video models to A/H detection.

pith-pipeline@v0.9.0 · 5644 in / 1098 out tokens · 68609 ms · 2026-05-10T15:56:49.245876+00:00 · methodology

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