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MC-LLaVA: Multi-Concept Personalized Vision-Language Model

Autor: An, Ruichuan, Yang, Sihan, Lu, Ming, Zeng, Kai, Luo, Yulin, Chen, Ying, Cao, Jiajun, Liang, Hao, She, Qi, Zhang, Shanghang, Zhang, Wentao

Current vision-language models (VLMs) show exceptional abilities across diverse tasks including visual question answering. To enhance user experience in practical applications, recent studies investigate VLM personalization to understand user-provide

Externí odkaz: http://arxiv.org/abs/2411.11706

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SAM-UNet:Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images

Autor: Yang, Sihan, Bi, Haixia, Zhang, Hai, Sun, Jian

Segment Anything Model (SAM) has demonstrated impressive performance on a wide range of natural image segmentation tasks. However, its performance significantly deteriorates when directly applied to medical domain, due to the remarkable differences b

Externí odkaz: http://arxiv.org/abs/2408.09886

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Calibrated Self-Rewarding Vision Language Models

Autor: Zhou, Yiyang, Fan, Zhiyuan, Cheng, Dongjie, Yang, Sihan, Chen, Zhaorun, Cui, Chenhang, Wang, Xiyao, Li, Yun, Zhang, Linjun, Yao, Huaxiu

Large Vision-Language Models (LVLMs) have made substantial progress by integrating pre-trained large language models (LLMs) and vision models through instruction tuning. Despite these advancements, LVLMs often exhibit the hallucination phenomenon, wh

Externí odkaz: http://arxiv.org/abs/2405.14622

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