arXiv Computer Vision October 1, 2026 By Ho-min Park, Byungkon Kang

Mutual Equilibrium: Multimodal Representation Learning through Reciprocal Feedback

Read the original on arXiv Computer Vision →

The paper introduces MEQ, a mutual feedback architecture that iteratively refines two multimodal inputs into coupled embeddings, each embedding incorporating information from the other. By continuously exchanging information between the modalities, the model converges to a fixed point that improves representation quality. Experiments on classification and visual grounding tasks show that MEQ achieves competitive or superior performance compared to concatenation-based baselines, and qualitatively enhances visual grounding when paired with complementary modalities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Hugging Face Trending Papers

3d ago

Mutual Equilibrium: Multimodal Representation Learning through Reciprocal Feedback

This work proposes a mutual feedback architecture, MEQ, that refines the two inputs, of possibly different modalities, into a pair of coupled embeddings such that each embedding reflects the informati...

rag multimodal

arXiv AI

Jun 11

Information-Theoretic Decomposition for Multimodal Interaction Learning

arXiv:2606. 11614v1 Announce Type: cross Abstract: Multimodal learning hinges on capturing redundant, unique, and synergistic information across modalities, which collectively constitute multimodal interactions.

By Zequn Yang, Yake Wei, Haotian Ni, Zhihao Xu, Di Hu

fine-tuning multimodal

arXiv Machine Learning

Jul 2

Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning

arXiv:2505. 19614v2 Announce Type: replace Abstract: Multimodal learning has seen remarkable progress, particularly with large-scale pre-training across various modalities.

By Sanghyuk Chun, Olga Russakovsky

multimodal benchmarks safety

arXiv Machine Learning

Jul 21

MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning

arXiv:2607. 16789v1 Announce Type: new Abstract: Real-world perception and decision making are inherently multimodal, integrating complementary signals across modalities.

By Sana Tonekaboni, Viktoria Schuster, Caroline Uhler

fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 4

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

arXiv:2606. 05109v1 Announce Type: new Abstract: To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information.

By Vasiliki Rizou, Pascal Frossard, Dorina Thanou

rag multimodal benchmarks safety

arXiv AI

4d ago

ResComEmb: Effective and Efficient Multimodal Embedding via Residual Homogeneity Compression

arXiv:2609.37225v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential for universal multimodal representation learning. However, existing methods eith...

By Zijing Cai, Yuzhe Wang, Jingxian Zhu, Fengbin Zhu, Richang Hong

llms rag multimodal

One email a morning, machine-written

Related stories

Mutual Equilibrium: Multimodal Representation Learning through Reciprocal Feedback

Information-Theoretic Decomposition for Multimodal Interaction Learning

Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning

MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

ResComEmb: Effective and Efficient Multimodal Embedding via Residual Homogeneity Compression