:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Weng, Tengjin, Jiang, Wenhao, Wang, Jingyi, Li, Ming, Ma, Lin, Ming, Zhong
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition
Online Access:	https://arxiv.org/abs/2603.09326
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

VisNumBench: Evaluating Number Sense of Multimodal Large Language Models
by: Weng, Tengjin, et al.
Published: (2025)

LlamaSeg: Image Segmentation via Autoregressive Mask Generation
by: Deng, Jiru, et al.
Published: (2025)

MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding
by: Zhu, Fengbin, et al.
Published: (2024)

Learning from Fine-Grained Visual Discrepancies: Mitigating Multimodal Hallucinations via In-Context Visual Contrastive Optimization
by: Deng, Haolin, et al.
Published: (2026)

ActionArt: Advancing Multimodal Large Models for Fine-Grained Human-Centric Video Understanding
by: Peng, Yi-Xing, et al.
Published: (2025)

VGRP-Bench: Visual Grid Reasoning Puzzle Benchmark for Large Vision-Language Models
by: Ren, Yufan, et al.
Published: (2025)

Predictive Regularization Against Visual Representation Degradation in Multimodal Large Language Models
by: Wang, Enguang, et al.
Published: (2026)

Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware Learning
by: Weng, Tengjin, et al.
Published: (2023)

Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding
by: He, Jinlong, et al.
Published: (2024)

VP-Bench: A Comprehensive Benchmark for Visual Prompting in Multimodal Large Language Models
by: Xu, Mingjie, et al.
Published: (2025)

ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models
by: Roberts, Jonathan, et al.
Published: (2025)

Attention-driven GUI Grounding: Leveraging Pretrained Multimodal Large Language Models without Fine-Tuning
by: Xu, Hai-Ming, et al.
Published: (2024)

MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models
by: Wang, Shengkang, et al.
Published: (2024)

MusiXQA: Advancing Visual Music Understanding in Multimodal Large Language Models
by: Chen, Jian, et al.
Published: (2025)

VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents
by: Liu, Xiao, et al.
Published: (2024)

EEE-Bench: A Comprehensive Multimodal Electrical And Electronics Engineering Benchmark
by: Li, Ming, et al.
Published: (2024)

EmoTrans: A Benchmark for Understanding, Reasoning, and Predicting Emotion Transitions in Multimodal LLMs
by: Hu, He, et al.
Published: (2026)

LookWise: Knowing When and Where to Look for Fine-Grained Visual Reasoning in Multimodal Large Language Models
by: Shen, Yuxiang, et al.
Published: (2026)

EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models
by: Shan, Haozhe, et al.
Published: (2026)

AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization
by: Liao, Jingyi, et al.
Published: (2025)

A Survey on Evaluation of Multimodal Large Language Models
by: Huang, Jiaxing, et al.
Published: (2024)

VER-Bench: Evaluating MLLMs on Reasoning with Fine-Grained Visual Evidence
by: Qiang, Chenhui, et al.
Published: (2025)

Democratizing Fine-grained Visual Recognition with Large Language Models
by: Liu, Mingxuan, et al.
Published: (2024)

Improving Visual Storytelling with Multimodal Large Language Models
by: Lin, Xiaochuan, et al.
Published: (2024)

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

Test-Time Computing for Referring Multimodal Large Language Models
by: Wu, Mingrui, et al.
Published: (2026)

Accelerating Multimodal Large Language Models via Dynamic Visual-Token Exit and the Empirical Findings
by: Wu, Qiong, et al.
Published: (2024)

ST$^3$: Accelerating Multimodal Large Language Model by Spatial-Temporal Visual Token Trimming
by: Zhuang, Jiedong, et al.
Published: (2024)

TextHawk: Exploring Efficient Fine-Grained Perception of Multimodal Large Language Models
by: Yu, Ya-Qi, et al.
Published: (2024)

You May Speak Freely: Improving the Fine-Grained Visual Recognition Capabilities of Multimodal Large Language Models with Answer Extraction
by: Lawrence, Logan, et al.
Published: (2025)

Towards Visual Text Grounding of Multimodal Large Language Model
by: Li, Ming, et al.
Published: (2025)

MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model
by: Jiang, Chaoya, et al.
Published: (2024)

What Kind of Visual Tokens Do We Need? Training-free Visual Token Pruning for Multi-modal Large Language Models from the Perspective of Graph
by: Jiang, Yutao, et al.
Published: (2025)

II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
by: Liu, Ziqiang, et al.
Published: (2024)

VideoPerceiver: Enhancing Fine-Grained Temporal Perception in Video Multimodal Large Language Models
by: Zhao, Fufangchen, et al.
Published: (2025)

VisuRiddles: Fine-grained Perception is a Primary Bottleneck for Multimodal Large Language Models in Abstract Visual Reasoning
by: Yan, Hao, et al.
Published: (2025)

Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
by: Ma, Chuofan, et al.
Published: (2024)

PhysVLM-AVR: Active Visual Reasoning for Multimodal Large Language Models in Physical Environments
by: Zhou, Weijie, et al.
Published: (2025)

Towards Fine-Grained Recognition with Large Visual Language Models: Benchmark and Optimization Strategies
by: Pang, Cong, et al.
Published: (2025)

Visual-Oriented Fine-Grained Knowledge Editing for MultiModal Large Language Models
by: Zeng, Zhen, et al.
Published: (2024)