Towards the Causal Complete Cause of Multi-Modal Representation Learning
Jingyao Wang, Siyu Zhao, Wenwen Qiang, Jiangmeng Li, Changwen Zheng, Fuchun Sun, Hui Xiong
2025Year
3Top-tier citations
Abstract
Multi-Modal Learning (MML) aims to learn effective representations across modalities for accurate predictions. Existing methods typically focus on modality consistency and specificity to learn effective representations. However, from a causal perspective, they may lead to representa-* Equal contribution
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Cited by top-tier papers3
- COPO: Causal-Oriented Policy Optimization for Hallucinations of MLLMsPeizheng Guo, Jingyao Wang, Wenwen Qiang, Jiahuan Zhou et al.CVPR 2026 · 1 citation
- TMAE: Learning Targeted Multi-Agent Exploration via Causal InferenceChuxiong Sun, Dunqi Yao, Rui Wang, Wenwen Qiang et al.AAAI 2026
- Advancing Complex Wide-Area Scene Understanding with Hierarchical Coresets SelectionJingyao Wang, Yiming Chen, Lingyu Si, Changwen ZhengACM MM 2025
Builds on34
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Scaling Up Visual and Vision-Language Representation Learning With Noisy Text SupervisionChao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen et al.ICML 2021 · 5,401 citations
- Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation LearningWeixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung et al.NeurIPS 2022 · 834 citations
- VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and TextHassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang et al.NeurIPS 2021 · 782 citations
- What Makes Multi-Modal Learning Better than Single (Provably)Yu Huang, Chenzhuang Du, Zihui Xue, Xuanyao Chen et al.NeurIPS 2021 · 404 citations
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