Solution for Emotion Prediction Competition of Workshop on Emotionally and Culturally Intelligent AI
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Abstract (arXiv)
This report provide a detailed description of the method that we explored and proposed in the WECIA Emotion Prediction Competition (EPC), which predicts a person's emotion through an artistic work with a comment. The dataset of this competition is ArtELingo, designed to encourage work on diversity across languages and cultures. The dataset has two main challenges, namely modal imbalance problem and language-cultural differences problem. In order to address this issue, we propose a simple yet effective approach called single-multi modal with Emotion-Cultural specific prompt(ECSP), which focuses on using the single modal message to enhance the performance of multimodal models and a well-designed prompt to reduce cultural differences problem. To clarify, our approach contains two main blocks: (1)XLM-Rciteconneau2019unsupervised based unimodal model and X2-VLMcitezeng2022x based multimodal model (2) Emotion-Cultural specific prompt. Our approach ranked first in the final test with a score of 0.627.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 26 Mar 2024.
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9 of 16 references.
References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
- 2021Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transportcited by 28
- 2021Exploiting Cross-Modal Prediction and Relation Consistency for Semi-Supervised Image Captioningcited by 23
- 2022ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culturecited by 17
- 2022X2-VLM: All-In-One Pre-trained Model For Vision-Language Taskscited by 9
- 2022Describing emotions with acoustic property prompts for speech emotion recognitioncited by 5
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- 2026ArtECulture: Benchmarking Culture-Conditioned Visual Emotion Understanding in Multimodal Large Language Models
- 2023Socratis: Are large multimodal models emotionally aware?
- 2025Deciphering Emotions in Children Storybooks: A Comparative Analysis of Multimodal LLMs in Educational Applications
- 2026Team LEYA in 10th ABAW Competition: Multimodal Ambivalence/Hesitancy Recognition Approach
- 2025Omni-Emotion: Extending Video MLLM with Detailed Face and Audio Modeling for Multimodal Emotion Analysis
- 2025Beyond Emotion Recognition: A Multi-Turn Multimodal Emotion Understanding and Reasoning Benchmark
- 2025Multimodal Large Language Models Meet Multimodal Emotion Recognition and Reasoning: A Survey
- 2025AffectGPT: A New Dataset, Model, and Benchmark for Emotion Understanding with Multimodal Large Language Models