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Feature Pyramid Network for Multi-task Affective Analysis

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Ruian He, Zhen Xing, Weimin Tan, Bo Yan

Computer Vision and Pattern Recognition · Submitted 8 Jul 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Extensive experiment results demonstrate the proposed model outperforms other models.

how it is stated · asserted 1.00 read the record →

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arXiv ID2107.03670 (version 3)
Pith IDIQTEZFNI, Pith's permanent record of this paper and its review
CitationBo Yan, Ruian He, Weimin Tan, Zhen Xing. "Feature Pyramid Network for Multi-task Affective Analysis." arXiv:2107.03670 (2021). https://pith.science/paper/IQTEZFNI
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,446 input tokens.

What the abstract itself states, as probabilities: claims first 0.09 · states limitation 0.06 · human participants 0.39 · controlled comparison 0.48 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.86 · mentions code or data availability 0.98.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

Affective Analysis is not a single task, and the valence-arousal value, expression class, and action unit can be predicted at the same time. Previous researches did not pay enough attention to the entanglement and hierarchical relation of these three facial attributes. We propose a novel model named feature pyramid networks for multi-task affect analysis. The hierarchical features are extracted to predict three labels and we apply a teacher-student training strategy to learn from pretrained single-task models. Extensive experiment results demonstrate the proposed model outperforms other models. This is a submission to The 2nd Workshop and Competition on Affective Behavior Analysis in the wild (ABAW). The code and model are available for research purposes at https://github.com/ryanhe312/ABAW2-FPNMAA.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Affective Analysis is not a single task, and the valence-arousal value, expression class, and action unit can be predicted at the same time. background 0.99 0.14 0.08 asserted 0.99
s1 Previous researches did not pay enough attention to the entanglement and hierarchical relation of these three facial attributes. gap 0.99 0.04 0.03 asserted 1.00
s2 We propose a novel model named feature pyramid networks for multi-task affect analysis. contribution 0.94 0.90 0.06 asserted 0.99
s3 The hierarchical features are extracted to predict three labels and we apply a teacher-student training strategy to learn from pretrained single-task models. method 0.98 0.94 0.06 asserted 0.99
s4 Extensive experiment results demonstrate the proposed model outperforms other models. result 1.00 0.94 0.80 asserted 1.00
s5 This is a submission to The 2nd Workshop and Competition on Affective Behavior Analysis in the wild (ABAW). background 0.81 0.34 0.02 asserted 0.99
s6 The code and model are available for research purposes at https://github.com/ryanhe312/ABAW2-FPNMAA. contribution 0.73 0.51 0.02 asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.98.

What was examined, and what was not

sources read
  • arXiv listing abstract v3
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Affective Analysis is not a single task, and the valence-arousal value, expression class, and action unit can be predicted at the same time. Previous researches did not pay enough attention to the entanglement and hierarchical relation of these three facial attributes. We propose a novel model named feature pyramid networks for multi-task affect analysis. The hierarchical features are extracted to predict three labels and we apply a teacher-student training strategy to learn from pretrained single-task models. Extensive experiment results demonstrate the proposed model outperforms other models. This is a submission to The 2nd Workshop and Competition on Affective Behavior Analysis in the wild (ABAW). The code and model are available for research purposes at https://github.com/ryanhe312/ABAW2-FPNMAA.

Human review

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites

Uses data or values from 4 papers
Cites as background 4 papers

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