Zobrazeno 1 - 10
of 579
pro vyhledávání: '"Li, Chunyuan"'
Autor:
Zhang, Peiyuan, Zhang, Kaichen, Li, Bo, Zeng, Guangtao, Yang, Jingkang, Zhang, Yuanhan, Wang, Ziyue, Tan, Haoran, Li, Chunyuan, Liu, Ziwei
Video sequences offer valuable temporal information, but existing large multimodal models (LMMs) fall short in understanding extremely long videos. Many works address this by reducing the number of visual tokens using visual resamplers. Alternatively
Externí odkaz:
http://arxiv.org/abs/2406.16852
Autor:
Xu, Lu, Zhu, Sijie, Li, Chunyuan, Kuo, Chia-Wen, Chen, Fan, Wang, Xinyao, Chen, Guang, Du, Dawei, Yuan, Ye, Wen, Longyin
The emerging video LMMs (Large Multimodal Models) have achieved significant improvements on generic video understanding in the form of VQA (Visual Question Answering), where the raw videos are captured by cameras. However, a large portion of videos i
Externí odkaz:
http://arxiv.org/abs/2406.10484
Autor:
Wang, Fei, Fu, Xingyu, Huang, James Y., Li, Zekun, Liu, Qin, Liu, Xiaogeng, Ma, Mingyu Derek, Xu, Nan, Zhou, Wenxuan, Zhang, Kai, Yan, Tianyi Lorena, Mo, Wenjie Jacky, Liu, Hsiang-Hui, Lu, Pan, Li, Chunyuan, Xiao, Chaowei, Chang, Kai-Wei, Roth, Dan, Zhang, Sheng, Poon, Hoifung, Chen, Muhao
We introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MuirBench consists of 12 diverse multi-image tasks (e.g., scene understanding, ordering) that involve 10 categories of
Externí odkaz:
http://arxiv.org/abs/2406.09411
Existing image-text modality alignment in Vision Language Models (VLMs) treats each text token equally in an autoregressive manner. Despite being simple and effective, this method results in sub-optimal cross-modal alignment by over-emphasizing the t
Externí odkaz:
http://arxiv.org/abs/2405.17871
In the field of graphic design, automating the integration of design elements into a cohesive multi-layered artwork not only boosts productivity but also paves the way for the democratization of graphic design. One existing practice is Graphic Layout
Externí odkaz:
http://arxiv.org/abs/2404.14368
Autor:
Zhang, Ruohong, Gui, Liangke, Sun, Zhiqing, Feng, Yihao, Xu, Keyang, Zhang, Yuanhan, Fu, Di, Li, Chunyuan, Hauptmann, Alexander, Bisk, Yonatan, Yang, Yiming
Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM). However, in tasks involving video instruction-following, providing informative
Externí odkaz:
http://arxiv.org/abs/2404.01258
Autor:
Chaves, Juan Manuel Zambrano, Huang, Shih-Cheng, Xu, Yanbo, Xu, Hanwen, Usuyama, Naoto, Zhang, Sheng, Wang, Fei, Xie, Yujia, Khademi, Mahmoud, Yang, Ziyi, Awadalla, Hany, Gong, Julia, Hu, Houdong, Yang, Jianwei, Li, Chunyuan, Gao, Jianfeng, Gu, Yu, Wong, Cliff, Wei, Mu, Naumann, Tristan, Chen, Muhao, Lungren, Matthew P., Chaudhari, Akshay, Yeung-Levy, Serena, Langlotz, Curtis P., Wang, Sheng, Poon, Hoifung
The scaling laws and extraordinary performance of large foundation models motivate the development and utilization of such models in biomedicine. However, despite early promising results on some biomedical benchmarks, there are still major challenges
Externí odkaz:
http://arxiv.org/abs/2403.08002
Autor:
Sun, Lichao, Huang, Yue, Wang, Haoran, Wu, Siyuan, Zhang, Qihui, Li, Yuan, Gao, Chujie, Huang, Yixin, Lyu, Wenhan, Zhang, Yixuan, Li, Xiner, Liu, Zhengliang, Liu, Yixin, Wang, Yijue, Zhang, Zhikun, Vidgen, Bertie, Kailkhura, Bhavya, Xiong, Caiming, Xiao, Chaowei, Li, Chunyuan, Xing, Eric, Huang, Furong, Liu, Hao, Ji, Heng, Wang, Hongyi, Zhang, Huan, Yao, Huaxiu, Kellis, Manolis, Zitnik, Marinka, Jiang, Meng, Bansal, Mohit, Zou, James, Pei, Jian, Liu, Jian, Gao, Jianfeng, Han, Jiawei, Zhao, Jieyu, Tang, Jiliang, Wang, Jindong, Vanschoren, Joaquin, Mitchell, John, Shu, Kai, Xu, Kaidi, Chang, Kai-Wei, He, Lifang, Huang, Lifu, Backes, Michael, Gong, Neil Zhenqiang, Yu, Philip S., Chen, Pin-Yu, Gu, Quanquan, Xu, Ran, Ying, Rex, Ji, Shuiwang, Jana, Suman, Chen, Tianlong, Liu, Tianming, Zhou, Tianyi, Wang, William, Li, Xiang, Zhang, Xiangliang, Wang, Xiao, Xie, Xing, Chen, Xun, Wang, Xuyu, Liu, Yan, Ye, Yanfang, Cao, Yinzhi, Chen, Yong, Zhao, Yue
Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Ther
Externí odkaz:
http://arxiv.org/abs/2401.05561
Autor:
Zhang, Hao, Li, Hongyang, Li, Feng, Ren, Tianhe, Zou, Xueyan, Liu, Shilong, Huang, Shijia, Gao, Jianfeng, Zhang, Lei, Li, Chunyuan, Yang, Jianwei
With the recent significant advancements in large multi-modal models (LMMs), the importance of their grounding capability in visual chat is increasingly recognized. Despite recent efforts to enable LMMs to support grounding, their capabilities for gr
Externí odkaz:
http://arxiv.org/abs/2312.02949
Autor:
Li, Feng, Jiang, Qing, Zhang, Hao, Ren, Tianhe, Liu, Shilong, Zou, Xueyan, Xu, Huaizhe, Li, Hongyang, Li, Chunyuan, Yang, Jianwei, Zhang, Lei, Gao, Jianfeng
In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities, but this idea is less explored in the vision domain. Existing visual prompting methods focus on referring segmentation to segment
Externí odkaz:
http://arxiv.org/abs/2311.13601