Abstract
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 information of the other. The core idea is to incorporate continuous interchange of information between the two inputs. This idea leads to a mutual feedback architecture consisting of two components whose outputs are fed back into the other. The final output of this model is defined as the fixed point of this interaction. We provide theoretical analysis that offers interpretation of this model as well as design choices to prevent failure cases. We show the benefits of MEQ through classification and visual grounding tasks spanning various datasets. Quantitatively, our model outperforms or shows competitive performance on concatenation-based multimodal classification problems. Qualitatively, the proposed interactive mechanism allows the model to progressively refine the visual grounding when paired with complementary modality, thus demonstrating the power of mutual feedback under such settings.
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Publication details
- Journal
- Not available
- Open access
- Green open access
Cite this article
APA 7
Park, H. M., & Kang, B. (2026). Mutual Equilibrium: Multimodal Representation Learning through Reciprocal Feedback. https://omanscience.com/en/articles/mutual-equilibrium-multimodal-representation-learning-through-reciprocal-feedback
MLA 9
Park, Ho-min, and Byungkon Kang. "Mutual Equilibrium: Multimodal Representation Learning through Reciprocal Feedback." https://omanscience.com/en/articles/mutual-equilibrium-multimodal-representation-learning-through-reciprocal-feedback.
Chicago (author–date)
Park, Ho-min, and Byungkon Kang. 2026. "Mutual Equilibrium: Multimodal Representation Learning through Reciprocal Feedback." https://omanscience.com/en/articles/mutual-equilibrium-multimodal-representation-learning-through-reciprocal-feedback.
Harvard
Park, H. M. and Kang, B. (2026) 'Mutual Equilibrium: Multimodal Representation Learning through Reciprocal Feedback', Available at: https://omanscience.com/en/articles/mutual-equilibrium-multimodal-representation-learning-through-reciprocal-feedback.
Vancouver
Park HM, Kang B. Mutual Equilibrium: Multimodal Representation Learning through Reciprocal Feedback. https://omanscience.com/en/articles/mutual-equilibrium-multimodal-representation-learning-through-reciprocal-feedback
IEEE
H. M. Park, and B. Kang, "Mutual Equilibrium: Multimodal Representation Learning through Reciprocal Feedback," https://omanscience.com/en/articles/mutual-equilibrium-multimodal-representation-learning-through-reciprocal-feedback.