arXiv AI
Jun 30
Multimodal Representation Alignment for Cross-modal Information Retrieval
arXiv:2506. 08774v2 Announce Type: replace-cross Abstract: Different machine learning models can represent the same underlying concept in different ways.
Multimodal Alignment Through Joint Kernel Entropic Gromov--Wasserstein Optimal Transport
arXiv:2608. 04234v1 Announce Type: cross Abstract: We study the problem of aligning data from multiple modalities into a shared representation space, focusing on settings where strong pretrained unimodal encoders are available but cross-modal paired data are scarce.
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arXiv Machine Learning
Jun 16
MUNI: Multimodal Unified Latent Diffusion for Coherent Any-to-Any Generation
arXiv:2606. 16408v1 Announce Type: new Abstract: We introduce MUNI, an end-to-end multimodal latent diffusion framework for any-to-any generation that unifies subset-conditioned cross-modal generation and unconditional joint sampling through a shared stochastic latent.
arXiv AI
Jun 30
SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport
arXiv:2602. 23353v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model of the world.
arXiv Machine Learning
Jul 28
Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning
arXiv:2605. 14981v2 Announce Type: replace Abstract: Gromov--Wasserstein (GW) distances compare graphs, shapes, and point clouds through internal distances, without requiring a common coordinate system.
arXiv Machine Learning
Jun 4
KODA: Contrastive Representation Comparison and Alignment for Vision-Language Foundation Models
arXiv:2606. 04180v1 Announce Type: new Abstract: Vision-language foundation models such as CLIP and SigLIP provide widely used representations for multimodal learning systems.
arXiv AI
Jun 30
Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation
arXiv:2606. 29464v1 Announce Type: cross Abstract: Vision-language dataset distillation (VLDD) compresses a large image-text paired dataset into a small set of synthetic pairs that can efficiently train contrastive vision-language models under strict data and compute budgets.
arXiv AI
Jul 21
Beyond Objective Expressivity: Geometry Preservation in Multimodal Contrastive Learning
arXiv:2607. 17673v1 Announce Type: cross Abstract: Contrastive learning is increasingly moving toward settings with three or more modalities instead of image-text pairs.
arXiv Machine Learning
Jun 9
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets
arXiv:2407. 01718v2 Announce Type: replace-cross Abstract: Embedding high-dimensional data into a low-dimensional space is an indispensable component of data analysis.
arXiv Machine Learning
Jul 14
Bures-Wasserstein Importance-Weighted Evidence Lower Bound: Exposition and Applications
arXiv:2602. 04272v2 Announce Type: replace-cross Abstract: The Importance-Weighted Evidence Lower Bound (IW-ELBO) has emerged as an effective objective for variational inference (VI), tightening the standard ELBO and mitigating the mode-seeking behaviour.
arXiv AI
Jun 8
Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models
arXiv:2602. 07026v3 Announce Type: replace-cross Abstract: Despite the success of multimodal contrastive learning in aligning visual and linguistic representations, a persistent geometric anomaly, the Modality Gap, remains: embeddings of distinct modalities expressing identical semantics occupy systematically offset regions.
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.
Hugging Face Trending Papers
Jun 3
RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities
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. Learning disentangled representations is a principled way to identify these underlying shared and unique factors that are hidden in observational data.