:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Ma, Ji, Suo, Wei, Wang, Peng, Zhang, Yanning
Formato:	Preprint
Publicado:	2025
Materias:	Computer Vision and Pattern Recognition
Acceso en línea:	https://arxiv.org/abs/2507.23362
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Pruning All-Rounder: Rethinking and Improving Inference Efficiency for Large Vision Language Models
por: Suo, Wei, et al.
Publicado: (2024)

Mitigating Information Loss under High Pruning Rates for Efficient Large Vision Language Models
por: Fu, Mingyu, et al.
Publicado: (2025)

C3L: Content Correlated Vision-Language Instruction Tuning Data Generation via Contrastive Learning
por: Ma, Ji, et al.
Publicado: (2024)

Understanding and Mitigating Hallucinations in Multimodal Chain-of-Thought Models
por: Ma, Ji, et al.
Publicado: (2026)

LVLM_CSP: Accelerating Large Vision Language Models via Clustering, Scattering, and Pruning for Reasoning Segmentation
por: Chen, Hanning, et al.
Publicado: (2025)

A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain Gap
por: Zhang, Lijun, et al.
Publicado: (2024)

LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression
por: Kundu, Souvik, et al.
Publicado: (2025)

Hallucination-aware intermediate representation edit in large vision-language models
por: Suo, Wei, et al.
Publicado: (2026)

Topology-Aware Layer Pruning for Large Vision-Language Models
por: Zheng, Pengcheng, et al.
Publicado: (2026)

LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models
por: Stan, Gabriela Ben Melech, et al.
Publicado: (2024)

A Glimpse to Compress: Dynamic Visual Token Pruning for Large Vision-Language Models
por: Zeng, Quan-Sheng, et al.
Publicado: (2025)

Prune Redundancy, Preserve Essence: Vision Token Compression in VLMs via Synergistic Importance-Diversity
por: Fang, Zhengyao, et al.
Publicado: (2026)

XDR-LVLM: An Explainable Vision-Language Large Model for Diabetic Retinopathy Diagnosis
por: Ito, Masato, et al.
Publicado: (2025)

TinyLVLM-eHub: Towards Comprehensive and Efficient Evaluation for Large Vision-Language Models
por: Shao, Wenqi, et al.
Publicado: (2023)

PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction
por: Xing, Long, et al.
Publicado: (2024)

LVLM-COUNT: Enhancing the Counting Ability of Large Vision-Language Models
por: Qharabagh, Muhammad Fetrat, et al.
Publicado: (2024)

INTERLACE: Interleaved Layer Pruning and Efficient Adaptation in Large Vision-Language Models
por: Madinei, Parsa, et al.
Publicado: (2025)

DiffV2IR: Visible-to-Infrared Diffusion Model via Vision-Language Understanding
por: Ran, Lingyan, et al.
Publicado: (2025)

Vision and Intention Boost Large Language Model in Long-Term Action Anticipation
por: Cao, Congqi, et al.
Publicado: (2025)

ViTCoP: Accelerating Large Vision-Language Models via Visual and Textual Semantic Collaborative Pruning
por: Luo, Wen, et al.
Publicado: (2026)

TransPrune: Token Transition Pruning for Efficient Large Vision-Language Model
por: Li, Ao, et al.
Publicado: (2025)

Language-Guided Token Compression with Reinforcement Learning in Large Vision-Language Models
por: Cao, Sihan, et al.
Publicado: (2026)

CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models
por: Tang, Zicong, et al.
Publicado: (2025)

Comb, Prune, Distill: Towards Unified Pruning for Vision Model Compression
por: Schmitt, Jonas, et al.
Publicado: (2024)

Video Compression Commander: Plug-and-Play Inference Acceleration for Video Large Language Models
por: Liu, Xuyang, et al.
Publicado: (2025)

Visual Prompt Selection for In-Context Learning Segmentation
por: Suo, Wei, et al.
Publicado: (2024)

Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration
por: Endo, Mark, et al.
Publicado: (2024)

Decoupled Similarity for Task-Aware Token Pruning in Large Vision-Language Models
por: Ma, Kexin, et al.
Publicado: (2026)

PLPHP: Per-Layer Per-Head Vision Token Pruning for Efficient Large Vision-Language Models
por: Meng, Yu, et al.
Publicado: (2025)

SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model
por: Ding, Zongcan, et al.
Publicado: (2025)

Enhancing Layer Attention Efficiency through Pruning Redundant Retrievals
por: Li, Hanze, et al.
Publicado: (2025)

LAPTOP-Diff: Layer Pruning and Normalized Distillation for Compressing Diffusion Models
por: Zhang, Dingkun, et al.
Publicado: (2024)

EfficientVLA: Training-Free Acceleration and Compression for Vision-Language-Action Models
por: Yang, Yantai, et al.
Publicado: (2025)

Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding
por: Suo, Wei, et al.
Publicado: (2025)

Investigating Redundancy in Multimodal Large Language Models with Multiple Vision Encoders
por: Wang, Yizhou, et al.
Publicado: (2025)

Global Compression Commander: Plug-and-Play Inference Acceleration for High-Resolution Large Vision-Language Models
por: Liu, Xuyang, et al.
Publicado: (2025)

Prune2Drive: A Plug-and-Play Framework for Accelerating Vision-Language Models in Autonomous Driving
por: Xiong, Minhao, et al.
Publicado: (2025)

LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging
por: Kim, Jinuk, et al.
Publicado: (2024)

MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for Accelerating Vision-Language Transformer
por: Cao, Jianjian, et al.
Publicado: (2024)

KVCapsule: Efficient Sequential KV Cache Compression for Vision-Language Models with Asymmetric Redundancy
por: Huang, Yingbing, et al.
Publicado: (2026)