Pith Number

pith:RFF3HE2C

pith:2026:RFF3HE2CYVH5UQZZBS4FC7DVRO

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

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

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

arxiv:2604.11730 v4 · 2026-04-13 · cs.CV · cs.HC · cs.LG

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4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

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.

C2weakest assumption

That standard deep learning architectures for video can capture subtle affective inconsistencies across and within modalities without major new adaptations for spatio-temporal fusion.

C3one line summary

Multimodal deep learning for ambivalence/hesitancy recognition in videos yields limited results on the BAH dataset, highlighting the need for improved spatio-temporal and cross-modal fusion methods.

Formal links

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Receipt and verification

First computed	2026-07-07T02:18:40.179022Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

894bb39342c54fda43390cb8517c758b84efdf18655f6a7498a503be70337dba

Aliases

arxiv: 2604.11730 · arxiv_version: 2604.11730v4 · doi: 10.48550/arxiv.2604.11730 · pith_short_12: RFF3HE2CYVH5 · pith_short_16: RFF3HE2CYVH5UQZZ · pith_short_8: RFF3HE2C

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/RFF3HE2CYVH5UQZZBS4FC7DVRO \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 894bb39342c54fda43390cb8517c758b84efdf18655f6a7498a503be70337dba

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "c7f7f843df43333e94e8e68d4d7eb893872b6b02f94b74b4ee849e8f73500137",
    "cross_cats_sorted": [
      "cs.HC",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-04-13T17:05:38Z",
    "title_canon_sha256": "b72966544afcc83e8a57c69ab66d7ff2f848820db1ac353945a44605aa1765da"
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    "kind": "arxiv",
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