most citedSTAR: Stage-Wise Attention-Guided Token Reduction for Efficient Large Vision-Language Models Inference

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cs.CV2026

STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models

Yichen Guo, Tinghao Wang, Qizhe Zhang +15

Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose substantial computational overhead,…

cs.CV2026

SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering

Kai Tang, Jinhao You, Bohua Zhang +6

Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual understanding tasks such as image captioning and visual question answering. However, they remain su…

cs.CV2026

WildRoadBench: A Wild Aerial Road-Damage Grounding Benchmark for Vision-Language Models and Autonomous Agents

Bingnan Liu, Chenhang Cui, Rui Huang +7

We introduce WildRoadBench, a wild aerial road-damage grounding benchmark that couples direct visual grounding by vision-language models with autonomous research-and-engineering by…

cs.CV2026

Models as Lego Builders: Assembling Malice from Benign Blocks via Semantic Blueprints

Chenxi Li, Xianggan Liu, Dake Shen +9

Despite the rapid progress of Large Vision-Language Models (LVLMs), the integration of visual modalities introduces new safety vulnerabilities that adversaries can exploit to elici…

cs.CV2025

MagicWand: A Universal Agent for Generation and Evaluation Aligned with User Preference

Zitong Xu, Dake Shen, Yaosong Du +3

Recent advances in AIGC (Artificial Intelligence Generated Content) models have enabled significant progress in image and video generation. However, users still struggle to obtain…

cs.CV2025

MAP: Mitigating Hallucinations in Large Vision-Language Models with Map-Level Attention Processing

Chenxi Li, Yichen Guo, Benfang Qian +5

Large Vision-Language Models (LVLMs) have achieved impressive performance in multimodal tasks, but they still suffer from hallucinations, i.e., generating content that is grammatic…

cs.CV2025

Enhancing Layer Attention Efficiency through Pruning Redundant Retrievals

Hanze Li, Yaosong Du, Zhibo Yao +3

Growing evidence suggests that layer attention mechanisms, which enhance interaction among layers in deep neural networks, have significantly advanced network architectures. Howeve…