116 citations · 264 across the 41 of their papers we have counts for
9 papers · 1 filter
Fluctuation-based Adaptive Structured Pruning for Large Language Models
Yongqi An, Xu Zhao, Tao Yu +2
Network Pruning is a promising way to address the huge computing resource demands of the deployment and inference of Large Language Models (LLMs). Retraining-free is important for…
Mitigating Hallucination in Visual Language Models with Visual Supervision
Zhiyang Chen, Yousong Zhu, Yufei Zhan +4
Large vision-language models (LVLMs) suffer from hallucination a lot, generating responses that apparently contradict to the image content occasionally. The key problem lies in its…
Continual Instruction Tuning for Large Multimodal Models
Jinghan He, Haiyun Guo, Ming Tang +1
Instruction tuning is now a widely adopted approach to aligning large multimodal models (LMMs) to follow human intent. It unifies the data format of vision-language tasks, enabling…
Griffon: Spelling out All Object Locations at Any Granularity with Large Language Models
Yufei Zhan, Yousong Zhu, Zhiyang Chen +3
Replicating the innate human ability to detect all objects based on free-form texts at any granularity remains a formidable challenge for Large Vision Language Models (LVLMs). Curr…
AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
Zhaopeng Gu, Bingke Zhu, Guibo Zhu +3
Large Vision-Language Models (LVLMs) such as MiniGPT-4 and LLaVA have demonstrated the capability of understanding images and achieved remarkable performance in various visual task…
Fast Segment Anything
Xu Zhao, Wenchao Ding, Yongqi An +5
The recently proposed segment anything model (SAM) has made a significant influence in many computer vision tasks. It is becoming a foundation step for many high-level tasks, like…