Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Chen Yukang"'
Autor:
Tang Yixin, Xu Miao, Luo Bo, Wang Yue, Chen Yukang, Yu Guangxi, Yang Guang, Gao Song, Wang Pei
Publikováno v:
Acta Biochimica et Biophysica Sinica, Vol 56, Pp 1089-1092 (2024)
Externí odkaz:
https://doaj.org/article/ff0d5698343949a89be8504beb6a5cd5
Autor:
Xue, Fuzhao, Chen, Yukang, Li, Dacheng, Hu, Qinghao, Zhu, Ligeng, Li, Xiuyu, Fang, Yunhao, Tang, Haotian, Yang, Shang, Liu, Zhijian, He, Ethan, Yin, Hongxu, Molchanov, Pavlo, Kautz, Jan, Fan, Linxi, Zhu, Yuke, Lu, Yao, Han, Song
Long-context capability is critical for multi-modal foundation models, especially for long video understanding. We introduce LongVILA, a full-stack solution for long-context visual-language models by co-designing the algorithm and system. For model t
Externí odkaz:
http://arxiv.org/abs/2408.10188
With the remarkable advancements in image generation and open-form text generation, the creation of interleaved image-text content has become an increasingly intriguing field. Multimodal story generation, characterized by producing narrative texts an
Externí odkaz:
http://arxiv.org/abs/2407.08683
Mathematical reasoning presents a significant challenge for Large Language Models (LLMs) due to the extensive and precise chain of reasoning required for accuracy. Ensuring the correctness of each reasoning step is critical. To address this, we aim t
Externí odkaz:
http://arxiv.org/abs/2406.18629
Autor:
Zeng, Zhongshen, Liu, Yinhong, Wan, Yingjia, Li, Jingyao, Chen, Pengguang, Dai, Jianbo, Yao, Yuxuan, Xu, Rongwu, Qi, Zehan, Zhao, Wanru, Shen, Linling, Lu, Jianqiao, Tan, Haochen, Chen, Yukang, Zhang, Hao, Shi, Zhan, Wang, Bailin, Guo, Zhijiang, Jia, Jiaya
Large language models (LLMs) have shown increasing capability in problem-solving and decision-making, largely based on the step-by-step chain-of-thought reasoning processes. However, evaluating these reasoning abilities has become increasingly challe
Externí odkaz:
http://arxiv.org/abs/2406.13975
Autor:
Peng, Bohao, Wu, Xiaoyang, Jiang, Li, Chen, Yukang, Zhao, Hengshuang, Tian, Zhuotao, Jia, Jiaya
The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models, especially in 3D semantic segmentation. However, sparse CNNs are still valuab
Externí odkaz:
http://arxiv.org/abs/2403.14418
Autor:
Liu, Shaoteng, Yuan, Haoqi, Hu, Minda, Li, Yanwei, Chen, Yukang, Liu, Shu, Lu, Zongqing, Jia, Jiaya
Large Language Models (LLMs) have demonstrated proficiency in utilizing various tools by coding, yet they face limitations in handling intricate logic and precise control. In embodied tasks, high-level planning is amenable to direct coding, while low
Externí odkaz:
http://arxiv.org/abs/2402.19299
Autor:
Ren, Tianhe, Liu, Shilong, Zeng, Ailing, Lin, Jing, Li, Kunchang, Cao, He, Chen, Jiayu, Huang, Xinyu, Chen, Yukang, Yan, Feng, Zeng, Zhaoyang, Zhang, Hao, Li, Feng, Yang, Jie, Li, Hongyang, Jiang, Qing, Zhang, Lei
We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM). This integration enables the detection and segmentation of any regions based on arbitrary text inputs and opens a do
Externí odkaz:
http://arxiv.org/abs/2401.14159
Autor:
Liu, Jiaheng, Bai, Zhiqi, Zhang, Yuanxing, Zhang, Chenchen, Zhang, Yu, Zhang, Ge, Wang, Jiakai, Que, Haoran, Chen, Yukang, Su, Wenbo, Ge, Tiezheng, Fu, Jie, Chen, Wenhu, Zheng, Bo
Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. Existing long-context extension methods usually need additional training procedures to support corresponding long-con
Externí odkaz:
http://arxiv.org/abs/2401.06951
In this paper, we propose a novel data-pruning approach called moving-one-sample-out (MoSo), which aims to identify and remove the least informative samples from the training set. The core insight behind MoSo is to determine the importance of each sa
Externí odkaz:
http://arxiv.org/abs/2310.14664