mlsanityData to 7 Oct 2026

Zero-shot Compound Expression Recognition with Visual Language Model at the 6th ABAW Challenge

arXiv 2403.11450 ↗First posted 18 Mar 2024cs.CVcited by 2

On the map: Multimodal LLMs / vision-language models → Multimodal emotion understanding

Abstract (arXiv)

Conventional approaches to facial expression recognition primarily focus on the classification of six basic facial expressions. Nevertheless, real-world situations present a wider range of complex compound expressions that consist of combinations of these basics ones due to limited availability of comprehensive training datasets. The 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW) offered unlabeled datasets containing compound expressions. In this study, we propose a zero-shot approach for recognizing compound expressions by leveraging a pretrained visual language model integrated with some traditional CNN networks.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Multimodal emotion understanding, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 12 of its 24 references that are in the map. First posted on arXiv 18 Mar 2024.

Builds on

12 of 24 references.

References · 12

  1. 2016Densely Connected Convolutional Networkscited by 6,691
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  3. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  4. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  5. 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
  6. 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
  7. 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
  8. 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
  9. 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
  10. 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155
  11. 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
  12. 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110

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Cited by year

Citing papers per year: 2024: 2.

2 papers.

  1. 20247th ABAW Competition: Multi-Task Learning and Compound Expression Recognitioncited by 53
  2. 2024HSEmotion Team at the 7th ABAW Challenge: Multi-Task Learning and Compound Facial Expression Recognitioncited by 6

Similar, not cited

Close in wording, no citation either way

  1. 2025Compound Expression Recognition via Large Vision-Language Models
  2. 2023EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition
  3. 2024Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge Transfer
  4. 2024ExpLLM: Towards Chain of Thought for Facial Expression Recognition
  5. 2025An Evaluation of a Visual Question Answering Strategy for Zero-shot Facial Expression Recognition in Still Images
  6. 2025Self-Supervised Multi-View Representation Learning using Vision-Language Model for 3D/4D Facial Expression Recognition
  7. 2025Facial Emotion Learning with Text-Guided Multiview Fusion via Vision-Language Model for 3D/4D Facial Expression Recognition
  8. 2026A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition