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REVIEW 2 major objections 6 minor 27 references

Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach

T0 review · 2 major / 6 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read A single text-anchored fusion model recognizes video-level ambivalence and hesitancy at 78.24% Macro F1 on a private test set, outperforming its text-only backbone by 4.03 percentage points.

desk verdict The four-point multimodal gain likely comes from richer text features in the fusion branch, not from audio/face/scene residuals. read the letter →

arxiv 2607.14702 v2 pith:RACWWDGX submitted 2026-07-16 cs.CV cs.AI

Elena Ryumina , Maxim Markitantov , Alexandr Axyonov , Fedor Shchetinin , Timur Abdulkadirov , Dmitry Ryumin , Alexey Karpov This is my paper · ORCID
classification cs.CVcs.AI
keywords ambivalencerecognitionhesitancymultimodalfusiontext-anchoredgatedresidualvideoclassificationmacroF1
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Ambivalence and hesitancy in video are hard to recognize because the signals are inconsistent across language, voice, face, and context; the best previous challenge systems handled this with large model ensembles. This paper claims that a single, text-anchored fusion model can do better: it treats the transcript as the primary signal and lets audio, face, and scene features make gated residual corrections to the text representation. On the challenge's evaluation subsets, text alone is the strongest unimodal input, and the fused model raises average Macro F1 from 72.33% over the development and public test subsets to 75.14%, reaching 78.24% on the private test subset — 4.03 points above text alone. The point is that complementary multimodal information improves recognition without needing an ensemble, which matters for practical deployment.

What carries the argument

Text Residual Fusion is the carrying mechanism. Text is the anchor modality; for each non-text modality m, a gate g_m = sigmoid(MLP([b; h_m])) is computed from the text representation b and the modality's projected features h_m, and the final fused representation is z = LayerNorm(b + sum_m g_m * d_m(h_m)), where d_m is a residual MLP. This keeps text as the primary source while letting audio, face, and scene inputs make input-dependent corrections; the fused vector is then passed to a two-layer MLP classifier.

What would settle it

Re-run the complete training protocol with many random seeds on fixed splits and compare Text Residual Fusion with the text-only model on the private test subset under the official evaluation script; if the 4-point advantage is not reproducible or changes sign, the claim that gated multimodal residuals improve over text alone is not established.

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Extended reading notes

Core claim

The paper's central claim is that, for video-level ambivalence and hesitancy recognition, multimodal fusion should be organized around text because transcripts carry the strongest unimodal signal. The proposed Text Residual Fusion model starts from the text representation and applies, for each of the audio, face, and scene modalities, a sigmoid gate computed from the text and that modality's features; the gated residual edits are summed and layer-normalized before classification. In the reported experiments, this single model reaches a Macro F1 of 76.13% on the development subset, 74.14% on the public test subset, and 78.24% on the private test subset, exceeding the text-only model by 4.03 p

Load-bearing premise

The central claim rests on the assumption that the single configuration chosen by automated search on the development subset, applied once to the test subsets, gives a representative estimate of the 4.03-point private-test gain over text alone.

Editorial extensions

If this is right

  • Text Residual Fusion beats the text-only model by 4.03 percentage points on the private test subset, so the paper supports the claim that non-text modalities carry complementary information that can be used inside a single model.
  • Prior top systems in this task relied on five to roughly twenty models; a single text-anchored model with comparable or better results would cut inference cost, memory, and implementation complexity.
  • The unimodal ranking (text stronger than audio, which is stronger than face and scene) suggests the task favors asymmetric fusion that invests capacity in the transcript.
  • The gated design gives the model a structural way to ignore an uninformative modality, since a gate can in principle approach zero.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper, the anchor-plus-gated-residual scheme could transfer to other affective states in which one modality dominates, such as confusion or sarcasm, with the gate acting as a learned reliability weight.
  • A testable extension would be to corrupt or drop each non-text modality at inference time; if the gates behave as reliability weights, performance should degrade gracefully and the gate values should fall for the corrupted modality.
  • The paper's selection procedure leaves open how much of the 4.03-point gain comes from the fusion architecture itself rather than favorable hyperparameters; a seeded re-run with variance reporting would separate those factors.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. This paper presents a text-centered multimodal fusion approach for the ABAW 11th Challenge's ambivalence/hesitancy (AH) recognition task. The proposed 'Text Residual Fusion' model anchors on text and uses gated residual adjustments from audio, face, and scene modalities. Experiments on the BAH corpus show the model achieves an average Macro F1 of 75.14% on Development/Public Test and 78.24% on Private Test, outperforming a text-only baseline by 4.03%. The paper claims this demonstrates that complementary multimodal information can improve AH recognition without a large ensemble.

Significance. If the central claim holds, the work offers a computationally efficient alternative to the ensemble-heavy top systems from the previous ABAW challenge, which is practically relevant. The paper provides a clear architectural description and evaluates on held-out test subsets. However, the comparison that supports the central claim is confounded, as detailed below. The paper does not include code, seeds, or exact hyperparameters, which limits reproducibility. With a matched text-only baseline or a disabling-non-text-gates ablation, the contribution could be properly established.

major comments (2)
  1. [§3.1, §3.5, Table 1] The central claim that multimodal fusion improves over text-only relies on a confounded comparison. The row-1 text baseline uses only the pooled [CLS] representation from RoBERTa-GoEmotions before its MLP head. In contrast, the fusion model's text branch (Eq. 12, §3.5) constructs inputs as u_text = [s_text; ℓ_text], where s_text = [μ_text, σ_text, μΔ_text, σΔ_text] are token-level summary statistics and ℓ_text are text logits. This is strictly more text-derived information than the baseline. The 4.03-point private-test gain could therefore be entirely due to the enriched text representation rather than to audio/face/scene residuals. No ablation is presented that either disables the non-text gates or trains a text-only version of the fusion architecture. To support the abstract claim, the authors should add a matched text-only baseline or an ablation that sets the non-text gates to zero.
  2. [§4.2, Table 1] The private-test improvement of 4.03% is reported as a single point estimate with no variance or statistical significance. The size of the Private Test subset is not stated, and no multiple-run or error-bar results are provided. Without uncertainty quantification, the reported difference may be within the noise of the evaluation. The authors should at least report the number of private-test videos and, ideally, standard deviations across seeds or bootstrap confidence intervals.
minor comments (6)
  1. [Title] The title contains a typo: 'ABA W' should be 'ABAW'.
  2. [§4.2] The text says 'Both fusion models outperform all unimodal configurations', but Table 1 presents only one fusion model (ID 5). This should be corrected to 'The Text Residual Fusion model'.
  3. [§3.3] Grammar: 'shorter sequences are remained unchanged' should be 'shorter sequences remain unchanged'.
  4. [§2.1] Grammar: 'an Multilayer Perceptron (MLP)' should be 'a Multilayer Perceptron (MLP)'.
  5. [§4.2] The selected Optuna hyperparameters (learning rate, dropout, hidden dimensions, batch size) are not reported, which hampers reproducibility. Please provide them in an appendix or supplementary material.
  6. [§4.1] The number of videos in the Private Test subset is not given. Adding this information would help readers interpret the reported MF1 values.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the fusion results are evaluated on a held-out test subset and the derivation does not reduce to its inputs.

full rationale

The paper's claim chain is empirical rather than definitional. Unimodal models (rows 1-4) are trained and tuned on the Development subset; the fusion model is selected with Optuna on Development and then applied to Public and Private Test subsets (Section 4.2). No fitted parameter is renamed as a prediction: the Private Test MF1 of 78.24% for Text Residual Fusion is a held-out evaluation. The text-anchor choice is justified by the unimodal results in Table 1 and by external BAH results [10], not by a self-citation chain. The self-citation [23] describes a prior ensemble system and is not load-bearing for the central claim. The reviewer's concern that the fusion text branch has richer text features (summary statistics u_m = [s_m; l_m] in Eq. 12) than the row-1 text baseline is a legitimate experimental-confound/correctness risk, not a circularity: the fusion model is not defined in terms of the target private-test score, and no equation in the paper makes the claimed improvement true by construction. Absence of code/seeds is a reproducibility limitation, not circular reasoning.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper's contribution is empirical; it relies on the BAH labels, pretrained features, and Optuna-selected hyperparameters. No new theoretical entities are introduced.

free parameters (4)
  • Auxiliary audio loss weights = 0.5 (token), 0.02 (smooth), 0.1 (event)
    Equation (5); authors state the weights were selected based on Development subset performance, making them fitted to the validation split.
  • Flow-matching weight lambda_FM = 0.1
    Equation (9); fixed at 0.1 without reported sensitivity analysis.
  • Optuna hyperparameters (LR, dropout, hidden dims, batch size) = not reported
    Section 4.2 says final configurations were selected on Development, but the exact values are omitted, preventing replication.
  • Architecture design choices = 4 frozen text layers, layer-10 audio features, 500 face frames, 16 scene frames
    Section 3; manual choices not justified by ablations.
assumptions (4)
  • domain assumption The BAH annotations and challenge evaluation protocol are reliable.
    Section 4.1; the paper takes dataset labels and challenge test labels as ground truth.
  • domain assumption Pre-trained feature extractors (RoBERTa-GoEmotions, Wav2Vec2-large-robust, EmoAffectNet, VideoMAE-v2) transfer to the BAH domain.
    Section 3; no domain adaptation or analysis of feature distribution shift is provided.
  • domain assumption Optuna-selected configuration generalizes from Development to Public/Private Test.
    Section 4.2; assumes repeated selection on Dev does not invalidate the Private Test estimate; no repeated splits are reported.
  • standard math Standard neural architecture building blocks work as specified.
    Section 3; relies on backpropagation, Transformer, Mamba, gating, and MLP properties.

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Cite this review

Pith. "Pith review of Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach." pith.science (2026). https://pith.science/paper/RACWWDGX

@misc{pith2026260714702,
  author       = {Pith},
  title        = {Pith review of: Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RACWWDGX}},
  note         = {Machine review of arXiv:2607.14702}
}
read the original abstract

Automatic recognition of ambivalence and hesitancy is challenging because these states may be expressed through inconsistent linguistic, acoustic, facial, and contextual patterns, while top-performing systems often rely on computationally expensive ensembles. We present a single text-centered multimodal approach for video-level ambivalence and hesitancy recognition for the 11th Affective & Behavior Analysis in-the-Wild (ABAW) Challenge. The proposed approach combines linguistic, acoustic, facial, and scene features using text-centered multimodal fusion model. Text Residual Fusion treats text as the anchor modality and applies gated residual adjustments based on the other modalities. Experiments on the Behavioural Ambivalence/Hesitancy (BAH) corpus confirm that text is the strongest unimodal modality. The Text Residual Fusion model achieves an average Macro F1-score (MF1) of 75.14% across the Development and Public Test subsets. On the Private Test subset, it reaches an MF1 of 78.24%, outperforming the text model by 4.03%. These results demonstrate that complementary multimodal information can improve recognition performance without requiring a large model ensemble.

Figures

Figures reproduced from arXiv: 2607.14702 by the authors.

Figure 1
Figure 1. Pipeline of text-centered multimodal approach [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Pipeline of the Text Residual Fusion model. 4 Experiments 4.1 Research Corpus The BAH corpus is the benchmark dataset for the AH task of the 11th ABAW Challenge. It was introduced for the multimodal AH recognition in realistic [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

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Works this paper leans on

27 extracted references · 2 canonical work pages

  1. [1]

    In: ACM SIGKDD Int

    Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In: ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. pp. 2623–2631 (2019).https://doi.org/10.1145/3292500. 3330701

    doi:10.1145/3292500 2019

  2. [2]

    In: NeurIPS

    Baevski, A., Zhou, Y., Mohamed, A., Auli, M.: wav2vec 2.0: A framework for self-supervised learning of speech representations. In: NeurIPS. vol. 33, pp. 12449– 12460 (2020).https://doi.org/10.48550/arXiv.2006.11477

    arXiv doi:10.48550/arxiv.2006.11477 2020

  3. [3]

    arXiv (2026).https://doi.org/10.48550/arXiv.2603.15818

    Bekhouche, S.E., Telli, H., Benlamoudi, A., Herrouz, S.E., Taleb-Ahmed, A., Ha- did, A.: Conflict-aware multimodal fusion for ambivalence and hesitancy recogni- tion. arXiv (2026).https://doi.org/10.48550/arXiv.2603.15818

    doi:10.48550/arxiv.2603.15818 2026

  4. [4]

    Bijkerk, L.E., Spigt, M., Oenema, A., Geschwind, N.: Engagement with mental health and health behavior change interventions: An integrative review of key concepts. J. Context. Behav. Sci.32, 100748 (2024).https://doi.org/10.1016/ j.jcbs.2024.100748

    arXiv 2024

  5. [5]

    In: ICCV

    Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: ICCV. pp. 9650– 9660 (2021).https://doi.org/10.1109/ICCV48922.2021.00951

    arXiv 2021

  6. [6]

    IEEE Trans

    Deng, J., Ren, F.: A survey of textual emotion recognition and its challenges. IEEE Trans. Affect. Comput.14(1), 49–67 (2023).https://doi.org/10.1109/TAFFC. 2021.3053275

    arXiv 2023

  7. [7]

    In: North Amer

    Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: North Amer. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol. vol. 1, pp. 4171–4186 (2019). https://doi.org/10.18653/v1/N19-1423

    doi:10.18653/v1/n19-1423 2019

  8. [8]

    In: CVPR

    Fang, Y., Huang, W., Wan, G., Su, K., Ye, M.: EMOE: Modality-specific enhanced dynamic emotion experts. In: CVPR. pp. 14314–14324 (2025).https://doi.org/ 10.1109/CVPR52734.2025.01335

    arXiv 2025

Show all 27 references
  1. [9]

    Neurocomputing568, 127015 (2024).https://doi.org/10.1016/j.neucom.2023.127015

    George, S.M., Ilyas, P.M.: A review on speech emotion recognition: A survey, re- cent advances, challenges, and the influence of noise. Neurocomputing568, 127015 (2024).https://doi.org/10.1016/j.neucom.2023.127015

    2024

  2. [10]

    In: ICLR (2026).https://doi.org/10.48550/arXiv.2505.19328

    González-González, M., Belharbi, S., Zeeshan, M.O., Sharafi, M., Aslam, M.H., Pedersoli, M., Lameiras Koerich, A., Bacon, S.L., Granger, E.: BAH dataset for ambivalence/hesitancy recognition in videos for digital behavioural change. In: ICLR (2026).https://doi.org/10.48550/arX...

    2026 doi arXiv

  3. [11]

    arXiv (2023).https://doi.org/10.48550/arXiv.2312.00752

    Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv (2023).https://doi.org/10.48550/arXiv.2312.00752

    2023 doi arXiv

  4. [13]

    Neural Comput.9(8), 1735–1780 (1997).https://doi.org/10.1162/neco.1997.9.8.1735

    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput.9(8), 1735–1780 (1997).https://doi.org/10.1162/neco.1997.9.8.1735

    1997 doi

  5. [14]

    arXiv (2026).https://doi.org/10.48550/arXiv.2603

    Jha, O.G., Bamniya, M., Borthakur, A.: Discriminative flow matching via local generative predictors. arXiv (2026).https://doi.org/10.48550/arXiv.2603. 13928

    2026 doi

  6. [16]

    In: ICCVW

    Kollias, D., Zafeiriou, S., Kotsia, I., Slabaugh, G., Senadeera, D.C., Zheng, J., Yadav, K.K.K., Shao, C., Hu, G.: From emotions to violence: Multimodal fine- grained behavior analysis at the 9th ABAW. In: ICCVW. pp. 1–12 (2025).https: //doi.org/10.1109/ICCVW69036.2025.00006

    2025

  7. [17]

    Leong, S.C., Tang, Y.M., Lai, C.H., Lee, C.K.M.: Facial expression and body ges- ture emotion recognition: A systematic review on the use of visual data in affective computing. Comput. Sci. Rev.48, 100545 (2023).https://doi.org/10.1016/j. cosrev.2023.100545

    2023

  8. [18]

    In: Empir

    Lin, B., Ye, Y., Zhu, B., Cui, J., Ning, M., Jin, P., Yuan, L.: Video-LLaVA: Learn- ing united visual representation by alignment before projection. In: Empir. Meth- ods Nat. Lang. Process. pp. 5971–5984. Association for Computational Linguistics (2024).https://doi.org/10.4855...

    2024 doi arXiv

  9. [19]

    arXiv (2019).https://doi.org/10.48550/arXiv.1907.11692

    Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: RoBERTa: A robustly optimized BERT pretraining approach. arXiv (2019).https://doi.org/10.48550/arXiv.1907.11692

    2019 doi arXiv

  10. [20]

    In: CVPRW

    Pereira, A., Barros, P., Fernandes, B.: BROTHER: Behavioral recognition opti- mized through heterogeneous ensemble regularization for ambivalence and hesi- tancy. In: CVPRW. pp. 5362–5369 (2026).https://doi.org/10.48550/arXiv. 2603.14361

    2026 doi

  11. [21]

    Poria, S., Cambria, E., Bajpai, R., Hussain, A.: A review of affective comput- ing: From unimodal analysis to multimodal fusion. Inf. Fusion37, 98–125 (2017). https://doi.org/10.1016/j.inffus.2017.02.003

    2017 doi

  12. [22]

    Nature323(6088), 533–536 (1986).https://doi.org/10

    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back- propagating errors. Nature323(6088), 533–536 (1986).https://doi.org/10. 1038/323533a0

    1986

  13. [23]

    In: CVPRW

    Ryumina, E., Axyonov, A., Sysoev, D., Abdulkadirov, T., Almetov, K., Mo- rozova, Y., Ryumin, D.: Ensemble-based prototype-augmented multimodal fu- sion for ambivalence/hesitancy recognition. In: CVPRW. pp. 5409–5418 (2026). https://doi.org/10.48550/arXiv.2603.12848

    2026 doi

  14. [24]

    Neurocomputing514, 435–450 (2022).https://doi.org/10.1016/j.neucom.2022.10.013

    Ryumina, E., Dresvyanskiy, D., Karpov, A.: In search of a robust facial expressions recognition model: A large-scale visual cross-corpus study. Neurocomputing514, 435–450 (2022).https://doi.org/10.1016/j.neucom.2022.10.013

    2022 doi

  15. [25]

    Sajjad, M., Ullah, F.U.M., Ullah, M., Christodoulou, G., Cheikh, F.A., Hijji, M., Muhammad, K., Rodrigues, J.J.P.C.: A comprehensive survey on deep facial ex- pression recognition: Challenges, applications, and future guidelines. Alex. Eng. J. 68, 817–840 (2023).https://doi.or...

    2023 doi

  16. [26]

    In: CVPRW

    Savchenko, A., Savchenko, L.: Leveraging lightweight facial models and textual modality in audio-visual emotional understanding in-the-wild. In: CVPRW. pp. 5824–5834 (2025).https://doi.org/10.1109/CVPRW67362.2025.00577

    2025

  17. [27]

    In: CVPRW

    Savchenko, A.V., Sidorova, A.P.: EmotiEffNet and temporal convolutional net- works in video-based facial expression recognition and action unit detection. In: CVPRW. pp. 4849–4859 (2024).https://doi.org/10.1109/CVPRW63382.2024. 00488

    2024

  18. [28]

    In: NeurIPS

    Vaswani,A.,Shazeer,N.,Parmar,N.,Uszkoreit,J.,Jones,L.,Gomez,A.N.,Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: NeurIPS. vol. 30, pp. 5998–6008 (2017).https://doi.org/10.5555/3295222.3295349

    2017

  19. [29]

    In: CVPR

    Wang, L., Huang, B., Zhao, Z., Tong, Z., He, Y., Wang, Y., Wang, Y., Qiao, Y.: VideoMAE V2: Scaling video masked autoencoders with dual masking. In: CVPR. pp. 14549–14560 (2023).https://doi.org/10.1109/CVPR52729.2023.01398

    2023

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Reviewed August 2, 2026 · model on record in the stance chip above.