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.
Spherical Interpolation for Backward-Compatible Multimodal Representations
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arXiv AI
4d ago
One Geometry, Different Outcomes: Readout-Dependent Effects of the Modality Gap in Vision-Language Models
The paper investigates how the modality gap— the separation between image and text representations in contrastive vision‑language models—affects different downstream tasks. By showing that a single dominant direction accounts for most of the image‑text mean separation, the authors explain why reducing or removing this gap can improve zero‑shot classification, degrade retrieval, or restore performance depending on the task. The study provides a geometric framework that clarifies when and why gap interventions should be applied in vision‑language systems.
arXiv Machine Learning
Aug 3
FORGE: Frame Orthogonality in Relevance Geometry for Long-Form Video Understanding
arXiv:2607. 25266v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have enabled long-form video understanding at a scale that was not previously possible.
arXiv Computer Vision
Sep 24
From Alignment to Fusion in 3D Vision-Language
arXiv:2609.28222v1 Announce Type: new Abstract: Unified 3D vision-language systems must combine complementary geometry, scale, and appearance cues while supporting tasks from instance segmentation to...
arXiv AI
Sep 1
Hyper3-CLIP: Hierarchy-Conditioned Hyperbolic Vision-Language Training
arXiv:2608.29313v1 Announce Type: cross Abstract: CLIP-like vision-language models (VLMs) trained with contrastive objectives learn strong global image-text representations, but their Euclidean embed...
arXiv Computer Vision
Sep 15
Query-Conditioned Spherical Centroid Aggregation for Multimodal Retrieval
The paper introduces SCALAR, a query‑conditioned spherical centroid aggregator that assigns relevance‑based weights to each available modality before computing a spherical centroid. SCALAR supports arbitrary modality subsets, is trained with rank‑8 LoRA adapters on masked views, and achieves positive aggregation gains on four of five benchmarks, outperforming prior symmetric aggregators. With only 4.8 million trainable parameters, SCALAR attains the highest text‑to‑video R@1 on three benchmarks and surpasses the released GRAM checkpoint under test‑time modality dropout by 3.2 to 10.9 R@1.
arXiv AI
Jun 16
Beyond Scalar Distances: Semantic Attribute Gradients from Frozen MLLMs for Visual Embeddings
arXiv:2606. 15134v1 Announce Type: cross Abstract: Vision encoders for retrieval are typically trained with class-label supervision: each training pair reduces to a scalar that uniformly pushes the embedding apart or pulls it together, as if every visual attribute either differed or matched.
arXiv AI
Sep 15
PACE: Progressive Angular-to-Norm Contrastive Embedding
arXiv:2609.15152v1 Announce Type: cross Abstract: Multimodal embedding models encode heterogeneous inputs into a shared embedding space, enabling efficient similarity computation across modalities an...
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...
Hugging Face Trending Papers
Sep 15
Multi-modal Knowledge Preserving Adapter for Embedding Backward Compatibility
Upgrading embedding models typically requires expensive database re-indexing, as new query embeddings are incompatible with existing database embeddings. While Backward Compatible Training (BCT) mitig...
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
Jun 26
Reconstruction Alignment Improves Unified Multimodal Models
arXiv:2509. 07295v4 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture.