Team RAS in 9th ABAW Competition: Multimodal Compound Expression Recognition Approach

Alexandr Axyonov; Alexey Karpov; Dmitry Ryumin; Elena Ryumina; Maxim Markitantov; Mikhail Dolgushin

REVIEW 4 major objections 4 minor 26 references

Team RAS in 9th ABAW Competition: Multimodal Compound Expression Recognition Approach

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A zero-shot six-modality pipeline recognizes compound emotions at levels comparable to supervised systems.

desk verdict A promising zero-shot CER pipeline whose headline numbers don't match its own table and whose scene-model training is ambiguous enough to threaten the zero-shot claim. read the letter →

arxiv 2507.02205 v2 pith:CXPTYDDR submitted 2025-07-02 cs.CV

Elena Ryumina , Maxim Markitantov , Alexandr Axyonov , Dmitry Ryumin , Mikhail Dolgushin , Alexey Karpov This is my paper

classification cs.CV

keywords compoundexpressionrecognitionzero-shotlearningmultimodalemotionvision-languagemodelmulti-headprobabilityfusionaffectivecomputingscenedescriptionfacial

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

This paper tries to establish that compound expressions—emotional states such as Happily Surprised or Sadly Fearful—can be recognized without any training on compound-expression labels. The authors assemble a six-modality pipeline (static and dynamic face, scene description, scene-label matching, audio, and text) whose components are either pretrained or trained only on eight basic-emotion labels, then fuse them with a learned weighting module and convert basic-emotion outputs into seven compound categories by pair-wise probability or feature aggregation. On the ABAW benchmark they report F1 of 46.95% on AffWild2, 49.02% on AFEW, and 34.85% on C-EXPR-DB, which they claim is comparable to supervised systems trained on target data. If correct, this would make compound-emotion recognition practical for new domains without collecting new labels, and would identify scene descriptions as the most informative modality for this task.

What carries the argument

The load-bearing machinery has three parts. First, Qwen-VL 2.5 turns each four-second video segment into a free-form textual scene description, which is then encoded by CLIP, DistilRoBERTa, or Jina embeddings and classified into eight basic-emotion labels; this scene modality alone produces the best C-EXPR-DB result. Second, the Multi-Head Probability Fusion (MHPF) module learns class-specific convex weights across modalities to combine probability distributions from the face, scene, audio, text, and label-matching models. Third, the Compound Expressions transformation maps eight basic-emotion outputs onto seven compound labels either by Pair-Wise Probability Aggregation (PPA), which sums the probabilities of the constituent basic emotions, or by Pair-Wise Feature Similarity Aggregation (PFSA), which averages basic-emotion feature prototypes in the latent space and matches test features by cosine similarity with temperature-scaled softmax.

What would settle it

Inspect the training logs and corpus annotations used in Section 3.3: if any C-EXPR-DB compound labels appear among the descriptions' training targets, the 34.85% F1 is not zero-shot. Separately, on a corpus with ground-truth basic and compound labels, compute whether the PPA pair-wise sum of predicted basic emotion probabilities matches the compound labels substantially better than a random or learned mapping would.

Watch

Extended reading notes

Core claim

The central claim is that a zero-shot pipeline can match supervised compound-expression recognizers. The best full pipeline achieves 46.95% F1 on AffWild2 and 49.02% on AFEW by fusing face, scene, audio, text, and scene-label modalities through the Multi-Head Probability Fusion module, while the best score on C-EXPR-DB (34.85% macro-F1) comes from a single scene model: Qwen-VL-generated descriptions of each four-second segment, encoded by a DistilRoBERTa-based emotion model, with pair-wise feature similarity aggregation used to form compound predictions. Against earlier work, the authors report that their scene model outperforms the zero-shot visual baseline (32.43%) and the zero-shot audio-visual-text baseline (25.91%), and trails only a curriculum-learning system that was fine-tuned on target compound data (60.63%).

Load-bearing premise

The claim stands or falls on the assumption that the scene-description model was trained without ever seeing target compound-expression labels, combined with the assumption that compound emotions are accurately formed by pair-wise sums of basic-emotion probabilities or feature prototypes.

Editorial extensions

If this is right

A new corpus with new compound labels can be handled by changing only the label text and prompt, without fine-tuning on target data.
Scene descriptions carry enough emotional signal that a single vision-language model can beat a full six-modality fusion on C-EXPR-DB.
Existing basic-emotion classifiers can be reused for compound recognition through the PPA/PFSA transformation, avoiding retraining on compound corpora.
Multimodal fusion helps most on large acted and in-the-wild corpora such as AffWild2 and AFEW, while its benefit on small compound-labeled corpora is less clear.

Reading between the lines

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

The authors do not test whether the PPA's additive assumption is true; a natural follow-up is to compare the fixed pair-wise sum against a learned composition function on a corpus annotated with both basic and compound labels.
The C-EXPR-DB success of the scene model suggests that descriptions may be a more transferable emotion signal than faces across domains; an ablation that feeds the same descriptions to a supervised classifier could separate the contribution of description content from the label-matching step.
If the zero-shot claim holds across more corpora, compound-expression recognition could be deployed for new emotion taxonomies, such as clinical or cultural categories, by editing the prompt and label set with no new annotation campaign.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a real attempt at zero-shot compound expression recognition with an unusual and potentially useful idea — using Qwen-VL scene descriptions as a modality — but the paper currently doesn't support its own headline numbers, and the one result that matters most (34.85 on C-EXPR-DB) depends on how the scene model was trained, which the paper doesn't specify. I wouldn't cite it in its current form, but I'd send it back for revision rather than desk reject.

What's genuinely new: the six-modality pipeline itself, and specifically the scene-description branch. Using a vision LLM to generate free-form or constrained descriptions of the scene, then encoding them with CLIP/RoBERTa/Jina and training a linear head, is a sensible way to inject context. The finding that the scene-based model alone beats the full multimodal fusion on C-EXPR-DB is interesting and worth checking. MHPF, PPA, and PFSA are reasonable engineering; PPA is close to Savchenko's probability aggregation, and PFSA's prototype construction is a minor twist, but the combination is not a copy.

Soft spots, in order of severity.

First, the scene model training protocol is ambiguous. Section 3.3 says descriptions are "aligned with compound emotion labels via corpus annotations" and prompt 2 outputs "exactly one of a predefined set of eight basic or seven compound emotion labels." Since AFEW and AffWild2 have no compound labels, the only source of compound annotations is C-EXPR-DB. If the linear head on the scene encoder was trained on C-EXPR-DB compound labels, then the 34.85 C-EXPR-DB result is supervised, not zero-shot, and the comparison with Savchenko and Richet in Table 2 is unfair. The paper doesn't state the label space or training corpus for the scene models, and it doesn't list this as a limitation. This is the load-bearing ambiguity.

Second, the abstract and conclusion report F1 scores of 46.95 on AffWild2 and 49.02 on AFEW, but no row in Table 1 has those numbers. The best fusion (row 17) gives 46.50 and 47.14 macro-F1, and the average column gives 48.77 and 47.34. I don't know where 46.95/49.02 come from. That's a serious reporting inconsistency. Table 2 also says 34.86 while the abstract says 34.85.

Third, smaller: no error bars or repeated runs, only the top three fusion combinations from an unspecified search, and the PFSA temperature is tuned on validation predictions. The AFEW "other" placeholder class is a reasonable choice but should be stated as a modeling decision.

The math looks straightforward and the source code is public, so these issues are checkable. The central idea—scene descriptions as a zero-shot signal for compound emotions—is worth pursuing even if the current writeup overclaims. I'd accept this for peer review but require the authors to clarify the scene-label protocol and reconcile the reported numbers with the table. If the scene model was indeed trained on C-EXPR-DB, the zero-shot claim collapses and the paper becomes a much smaller contribution.

Referee Report

4 major / 4 minor

Summary. The paper describes a multimodal compound expression recognition (CER) pipeline for the ABAW competition setting. It combines static and dynamic facial models, CLIP-based label matching, Qwen-VL scene descriptions, audio, and text, and it introduces a Multi-Head Probability Fusion (MHPF) module plus two compound-expression mapping methods, PPA and PFSA. The authors report that the pipeline achieves macro-F1 scores of 46.95% on AffWild2, 49.02% on AFEW, and 34.85% on C-EXPR-DB without fine-tuning on target compound-expression labels, and they compare these results with prior supervised and zero-shot CER methods.

Significance. If the central claim holds, this is a useful result for affective computing: it would show that compound expressions can be recognized competitively without training on target compound labels, which matters for low-resource CER settings. The paper's strengths include a public code release, a broad set of modalities, and an explicit comparison with prior zero-shot and fine-tuned methods. However, the significance is conditional on resolving a training-protocol ambiguity for the scene model and on reconciling the reported headline numbers with the experimental table.

major comments (4)

[Abstract and §5 vs. Table 1] The abstract and conclusion report F1 scores of 46.95% on AffWild2 and 49.02% on AFEW, but Table 1 row 17 reports macro-F1 values of 46.50 and 47.14 for those datasets. No row in Table 1 matches the abstract/conclusion values. Please state exactly which configuration and metric produce 46.95 and 49.02, or correct the text to match the table.
[§3.3 and §4.1] The training protocol for the scene models is ambiguous and directly affects the zero-shot claim. The text says generated descriptions are 'aligned with compound emotion labels via corpus annotations' and that prompt 2 outputs one of eight basic or seven compound labels, and a linear classification head is added to each encoder. If that head is trained on C-EXPR-DB compound labels, the reported 34.85 macro-F1 on C-EXPR-DB is a supervised result, not a zero-shot result. Please specify the exact training corpus and label space for each scene model and clarify how corpus annotations are used.
[§3.7] The PPA and PFSA mappings assume compound emotions are additive combinations of basic emotions, either at the probability level or in averaged feature space. This assumption is not validated against ground truth. Please add an ablation or analysis comparing PPA/PFSA with a simple learned mapping on the validation set, and report per-class performance for the compound classes, so the reader can see whether the method works uniformly or is driven by a few classes.
[§4.2 and Table 1] No error bars or repeated runs are reported, and only the top three fusion combinations from an unspecified search are shown. The difference between the best C-EXPR-DB result (34.85) and the prior zero-shot baseline (32.43) may not be meaningful without variance information. Please report the number of seeds, the search space considered, and the performance distribution over those configurations.

minor comments (4)

[Table 2] Table 2 lists 'Ours ZH V' with a score of 34.86, but Table 1 row 7 reports 34.85, and the model actually uses scene descriptions and a text encoder in addition to visual input, so the modality label 'V' is misleading.
[§3.4 and Table 1] The audio model is described as 'U-Net-aware' in the text but is labeled 'U-aware' in Table 1 row 9; please use consistent terminology.
[Figure 1] The pipeline figure is very dense, and the labels for individual modality models are hard to read; a larger figure or a tabular summary of each model's input, backbone, and classification head would improve clarity.
[§1 and §5] The abstract lists five modality groups but the conclusion says six modalities, with 'probabilistic fusion' counted as a modality; please clarify whether fusion is a modality or a processing stage.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: compound mapping is not fitted to target labels; scene-model wording is ambiguous but not a demonstrated reduction.

full rationale

The paper's derivation chain is self-contained: basic-emotion classifiers are trained on AFEW and AffWild2 (§4.1), and compound outputs are obtained by PPA (summing basic-emotion probabilities) or PFSA (cosine similarity to prototypes averaged from basic-emotion prototypes) in §3.7. Neither PPA nor PFSA uses C-EXPR-DB compound labels during training; the compound prototypes are built from validation features of correctly classified samples, and no target-corpus labels enter the mapping. The §3.3 phrase 'aligned with compound emotion labels via corpus annotations' is ambiguous, but §4.1 states that models are trained on AFEW and AffWild2 and that C-EXPR-DB is used only for evaluation, and §3.7 explicitly operates on eight basic emotion labels before conversion. Thus the best C-EXPR-DB score is not shown to be a supervised result renamed as zero-shot. Self-citations to prior RAS work ([21], [22]) are contextual or component-level and are not load-bearing; no uniqueness theorem or ansatz is smuggled in via citation. The paper's main weakness is a reporting ambiguity, not circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central CER result rests on several assumptions about how basic emotions compose into compound emotions, the faithfulness of LLM-generated scene descriptions, and the label-alignment between corpora. The additive/mixture assumption behind PPA and PFSA is not validated with target labels, so it carries much of the interpretive load.

free parameters (4)

PFSA softmax temperature = not reported
Temperature scaling in Section 3.7 controls sharpness of compound-emotion distribution; its value is not given and it is presumably tuned on validation, directly affecting the 34.85 PFSA score.
MHPF fusion weights = learned (not reported)
Section 3.6 defines learnable per-head and per-class weights; these are fit to training data and determine the 46.95/49.02 headline numbers.
AFEW 'other' placeholder class = added as 8th class
Section 4.1 introduces a placeholder 'other' label for AFEW to align with AffWild2; this hand-chosen mapping affects multi-corpus training and downstream CE mapping.
Segment window and sampling = 4 s window, 2 s overlap, 20 fps
Section 3 states the window and frame downsampling; these are design choices from prior studies that affect temporal emotion modeling for all modalities.

assumptions (5)

domain assumption Compound emotions decompose as additive combinations of basic emotion probabilities (PPA).
Section 3.7 states compound probabilities are sums of basic emotion probabilities; unvalidated against ground-truth compound labels.
domain assumption Compound emotion prototypes can be approximated by averaging basic-emotion prototypes in feature space (PFSA).
Section 3.7 constructs compound prototypes by averaging basic prototypes after L2 normalization; no evidence this holds in the learned latent space.
domain assumption Qwen-VL generated scene descriptions faithfully capture emotional content.
Section 3.3 uses Qwen-VL descriptions as input modality; no quality check or human evaluation of generated descriptions is reported.
domain assumption The 8-class label spaces of AffWild2 and AFEW are compatible after adding the 'other' placeholder.
Section 4.1 aligns the two corpora by adding a placeholder class, assuming no semantic mismatch for the remaining classes.
domain assumption CLIP cosine similarity between frames and emotion label texts provides meaningful zero-shot emotion probabilities.
Section 3.2 relies on CLIP for zero-shot classification; no calibration or validation of CLIP's emotion semantics is reported.

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Cite this review

Pith. "Pith review of Team RAS in 9th ABAW Competition: Multimodal Compound Expression Recognition Approach." pith.science (2026). https://pith.science/paper/CXPTYDDR

@misc{pith2026250702205,
  author       = {Pith},
  title        = {Pith review of: Team RAS in 9th ABAW Competition: Multimodal Compound Expression Recognition Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CXPTYDDR}},
  note         = {Machine review of arXiv:2507.02205}
}

read the original abstract

Compound Expression Recognition (CER), a subfield of affective computing, aims to detect complex emotional states formed by combinations of basic emotions. In this work, we present a novel zero-shot multimodal approach for CER that combines six heterogeneous modalities into a single pipeline: static and dynamic facial expressions, scene and label matching, scene context, audio, and text. Unlike previous approaches relying on task-specific training data, our approach uses zero-shot components, including Contrastive Language-Image Pretraining (CLIP)-based label matching and Qwen-VL for semantic scene understanding. We further introduce a Multi-Head Probability Fusion (MHPF) module that dynamically weights modality-specific predictions, followed by a Compound Expressions (CE) transformation module that uses Pair-Wise Probability Aggregation (PPA) and Pair-Wise Feature Similarity Aggregation (PFSA) methods to produce interpretable compound emotion outputs. Evaluated under multi-corpus training, the proposed approach shows F1 scores of 46.95% on AffWild2, 49.02% on Acted Facial Expressions in The Wild (AFEW), and 34.85% on C-EXPR-DB via zero-shot testing, which is comparable to the results of supervised approaches trained on target data. This demonstrates the effectiveness of the proposed approach for capturing CE without domain adaptation. The source code is publicly available.

Figures

Figures reproduced from arXiv: 2507.02205 by the authors.

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

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Wavlm: Large-scale self- supervised pre-training for full stack speech processing

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work page 2022

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Affect analysis in-the-wild: Valence-arousal, expressions, action units and a unified framework

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Analysing affective behavior in the first abaw 2020 competition

D Kollias, A Schulc, E Hajiyev, and S Zafeiriou. Analysing affective behavior in the first abaw 2020 competition. In International Conference on Automatic Face and Gesture Recognition (FG), pages 794–800, 2020. 5

work page 2020