1 citations · 1 across the 9 of their papers we have counts for
10 papers
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,…
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…
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…
Mitigating Hallucinations in Large Language Models Via Decoder Layer Skipping
Hanze Li, Jinhao You, Yichen Guo +3
Large Language Models (LLMs) have achieved strong performance across diverse natural language tasks, yet their outputs often suffer from hallucinations -- content that is misaligne…
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…
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…
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…
STAR: Stage-Wise Attention-Guided Token Reduction for Efficient Large Vision-Language Models Inference
Yichen Guo, Hanze Li, Zonghao Zhang +3
Although large vision-language models (LVLMs) leverage rich visual token representations to achieve strong performance on multimodal tasks, these tokens also introduce significant…
Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models
Kai Tang, Jinhao You, Yichen Guo +8
Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucinations, where generated content is inconsistent with the input image…
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…