Hugging Face Trending Papers
Jun 9
Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm
Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e. g.
Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm
arXiv:2606. 10504v1 Announce Type: new Abstract: Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e.
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arXiv AI
Jun 3
Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model Enhancement
arXiv:2412. 01282v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) bring powerful understanding and reasoning capabilities to multimodal tasks.
arXiv AI
Jul 14
PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging
arXiv:2604. 22823v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) rely on multimodal pre-training over diverse data sources, where different datasets often induce complementary cross-modal alignment capabilities.
arXiv Computer Vision
Aug 28
LLaVAFlow: Preserving Latent Alignment Flow for Parameter-Efficient Multimodal Fine-Tuning
LLaVAFlow is an information‑theoretic distillation framework designed to preserve cross‑modal alignment in Multimodal Large Language Models during visual instruction tuning. It compresses the mutual information between extracted relations and MLLM embeddings to refine alignment flow, and then maximizes mutual information between pretrained and fine‑tuned alignment flows to transfer compact alignment information. Experiments demonstrate that LLaVAFlow effectively maintains alignment flow, improving downstream performance and generalization.
arXiv AI
4d ago
Codebook-Guided Cross-Modal Knowledge Distillation for Structurally Heterogeneous Features
arXiv:2609.37243v1 Announce Type: cross Abstract: Cross-modal knowledge distillation transfers knowledge from a teacher modality to a student modality. Existing feature-level alignment methods typica...
arXiv Computer Vision
Sep 11
CLIP-RD: Relational Distillation for Efficient CLIP Knowledge Distillation
CLIP-RD introduces a relational distillation framework for efficient CLIP knowledge distillation, featuring Vertical Relational Distillation (VRD) and Cross Relational Distillation (XRD). VRD aligns intra‑modal similarity distributions between teacher and student, while XRD aligns cross‑modal similarity distributions to enforce bidirectional symmetry. This joint modeling of multidirectional relational structures improves the student’s embedding geometry, yielding a 1.8%p performance gain over CLIP‑KD across various architectures, tasks, and corruption settings with minimal training‑time overhead.
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
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.
arXiv AI
Jul 7
SpectralGCD: Spectral Concept Selection and Cross-modal Representation Learning for Generalized Category Discovery
arXiv:2602. 17395v2 Announce Type: replace-cross Abstract: Generalized Category Discovery (GCD) aims to identify novel categories in unlabeled data while leveraging a small labeled subset of known classes.
arXiv Computation and Language
Aug 24
When Better Teachers Don't Make Better Students: Revisiting Knowledge Distillation for CLIP Models in VQA
arXiv:2511.17886v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have achieved remarkable success across multimodal tasks, yet their substantial computational demands hinder ef...
Hugging Face Trending Papers
Jul 23
UnDA: Unpaired Domain Alignment for Cross-Modal Knowledge Transfer in Medical Imaging
Multimodal based approaches often outperform single modality approaches in downstream tasks as the different modalities provide complementary information, yet acquiring paired clinical data remains a significant challenge in real world scenarios. While cross-modal knowledge distillation addresses this, existing methods often struggle with large modality gaps and the propagation of noise from uncertain source-domain predictions.
arXiv Computation and Language
3d ago
MCD: Causal Distillation of Multimodal In-Context Learning in Large Vision-Language Models
arXiv:2609.39920v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) exhibit strong multimodal in-context learning (ICL) capabilities, yet this ability degrades substantially as mod...