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This paper introduces MEQ, a multimodal architecture utilizing reciprocal feedback loops to create coupled embeddings that converge at a fixed point. Theoretical analysis and empirical results on classification and visual grounding tasks demonstrate that this iterative refinement mechanism outperforms or competes with standard concatenation-based approaches. By enabling continuous information interchange between different modalities, the model offers a robust framework for handling complementary data inputs in complex perception tasks.
Mutual Equilibrium: Multimodal Representation Learning through Reciprocal Feedback
arXiv:2609.39456v1 Announce Type: new 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.
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.
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2609.39456 [cs.LG]
(or arXiv:2609.39456v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.39456 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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