arXiv AI

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

Jun 10

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.

By Trong Khiem Tran, Anh Duc Chu, Quang Hung Pham, Phi Le Nguyen, Trong Nghia Hoang

efficiency multimodal benchmarks safety

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...

By Pume Tuchinda, Parinthapat Pengpun, Romrawin Chumpu, Patomporn Payoungkhamdee, Sarana Nutanong, Peerat Limkonchotiwat

llms nlp efficiency multimodal benchmarks

arXiv AI

Jun 24

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment

arXiv:2509. 14001v5 Announce Type: replace-cross Abstract: Personalized object detection aims to adapt a general-purpose detector to recognize user-specific instances from only a few examples.

By Elena Camuffo, Francesco Barbato, Mete Ozay, Simone Milani, Umberto Michieli

llms rag computer-vision efficiency multimodal benchmarks safety

arXiv AI

Jun 29

Large Language Model Teaches Visual Students: Cross-Modality Transfer of Fine-Grained Conceptual Knowledge

arXiv:2606. 27527v1 Announce Type: cross Abstract: Large Language Models (LLMs) possess broad conceptual knowledge acquired through large-scale text pretraining, yet their potential to supervise models in other modalities remains underexplored.

By Thomas Shih-Chao Liang, Zhuoran Yu, Yong Jae Lee

llms efficiency multimodal benchmarks

arXiv AI

Jun 29

Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs

arXiv:2601. 22709v5 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) achieve strong multimodal performance but are costly to deploy, and post-training quantization often causes significant accuracy loss.

By Yanlong Chen, Amirhossein Habibian, Luca Benini, Yawei Li

llms efficiency multimodal benchmarks safety

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.

efficiency multimodal benchmarks safety

arXiv AI

Jun 2

Visual-Noise Guided In-Context Distillation for Multimodal Large Language Model Unlearning

arXiv:2606. 00105v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress on vision-language tasks, but they may also memorize and expose sensitive or restricted knowledge, raising concerns about privacy and broader safety risks.

By Junkai Chen, Yuhao He, Junxiang You, Ruiqi Liu, Chenyu Wang, Shu Wu

llms efficiency multimodal safety

arXiv Machine Learning

Aug 4

Distill What the Student Can See: Fisher-Projected On-Policy Distillation for Vision-Language Models

arXiv:2608. 01263v1 Announce Type: new Abstract: On-policy distillation (OPD) samples trajectories from the current student policy and minimizes token-level divergence between student and teacher next-token distributions at prefixes along those trajectories.

By Leyan Xue, Feng Xiong, Mingjun Ma, Changqing Zhang

llms efficiency multimodal benchmarks

arXiv Computation and Language

Sep 10

On-Policy Distillation for Vision-Language Model Adaptation, an Effective Paradigm on Low-Quality Multimodal Data

arXiv:2609.10321v1 Announce Type: new Abstract: Knowledge distillation offers an efficient route to transfer a task-adapted vision-language teacher to a compact student. The training target in curren...

By Hongyuan Zhang, Xianda Guo, Yanlun Peng, Qianlong Yang, Yubin Guo, Pinhan Fu, Mulin Chen, Xiaozhen Qiao, Ping Luo

llms efficiency multimodal benchmarks

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...

By Yanshu Li, Jiaqian Li, Canran Xiao, Xi Xiao, Tianyang Wang, Yongtai Liu

llms efficiency multimodal benchmarks safety

arXiv AI

Jul 1

Knowledge Distillation from Large Reasoning Models to Compact Student Models: A Case Study on the John O Bryan Mathematics Competition

arXiv:2606. 31048v1 Announce Type: cross Abstract: This paper investigates knowledge distillation from a large reasoning model (DeepSeek-R1) to a compact student model (Qwen2.

By Gaurab Baral, Aaditya Khanal, Yangyang Tao, Junxiu Zhou

agents fine-tuning efficiency benchmarks

arXiv AI

Jul 2

Cross4D-JEPA: Dense Cross-modal Correspondence Distillation for 4D Point Cloud Representation Learning

arXiv:2607. 00514v1 Announce Type: cross Abstract: Automatic understanding of dynamic 4D point clouds, the 3D-point sequences captured over time by depth sensors and LiDAR, is central to robotics and embodied perception.

By Trung Thanh Nguyen, Hai Nguyen-Truong, Tu Vo, Hoang M. Truong, Tuan-Anh Vu

rag diffusion robotics fine-tuning efficiency multimodal benchmarks

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