Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Cui, Erfei"'
Autor:
Li, Qingyun, Chen, Zhe, Wang, Weiyun, Wang, Wenhai, Ye, Shenglong, Jin, Zhenjiang, Chen, Guanzhou, He, Yinan, Gao, Zhangwei, Cui, Erfei, Yu, Jiashuo, Tian, Hao, Zhou, Jiasheng, Xu, Chao, Wang, Bin, Wei, Xingjian, Li, Wei, Zhang, Wenjian, Zhang, Bo, Cai, Pinlong, Wen, Licheng, Yan, Xiangchao, Li, Zhenxiang, Chu, Pei, Wang, Yi, Dou, Min, Tian, Changyao, Zhu, Xizhou, Lu, Lewei, Chen, Yushi, He, Junjun, Tu, Zhongying, Lu, Tong, Wang, Yali, Wang, Limin, Lin, Dahua, Qiao, Yu, Shi, Botian, He, Conghui, Dai, Jifeng
Image-text interleaved data, consisting of multiple images and texts arranged in a natural document format, aligns with the presentation paradigm of internet data and closely resembles human reading habits. Recent studies have shown that such data ai
Externí odkaz:
http://arxiv.org/abs/2406.08418
Autor:
Chen, Zhe, Wang, Weiyun, Tian, Hao, Ye, Shenglong, Gao, Zhangwei, Cui, Erfei, Tong, Wenwen, Hu, Kongzhi, Luo, Jiapeng, Ma, Zheng, Ma, Ji, Wang, Jiaqi, Dong, Xiaoyi, Yan, Hang, Guo, Hewei, He, Conghui, Shi, Botian, Jin, Zhenjiang, Xu, Chao, Wang, Bin, Wei, Xingjian, Li, Wei, Zhang, Wenjian, Zhang, Bo, Cai, Pinlong, Wen, Licheng, Yan, Xiangchao, Dou, Min, Lu, Lewei, Zhu, Xizhou, Lu, Tong, Lin, Dahua, Qiao, Yu, Dai, Jifeng, Wang, Wenhai
In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements: (
Externí odkaz:
http://arxiv.org/abs/2404.16821
We study the challenging problem for inference tasks on large-scale graph datasets of Graph Neural Networks: huge time and memory consumption, and try to overcome it by reducing reliance on graph structure. Even though distilling graph knowledge to s
Externí odkaz:
http://arxiv.org/abs/2403.01079
Autor:
Liu, Zhaoyang, Lai, Zeqiang, Gao, Zhangwei, Cui, Erfei, Li, Ziheng, Zhu, Xizhou, Lu, Lewei, Chen, Qifeng, Qiao, Yu, Dai, Jifeng, Wang, Wenhai
We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks. Despite the remarkable performance of LLMs, they still struggle with tool invocation due to ambiguou
Externí odkaz:
http://arxiv.org/abs/2310.17796