Zobrazeno 1 - 10
of 290
pro vyhledávání: '"WANG Qimeng"'
Publikováno v:
Guan'gai paishui xuebao, Vol 43, Iss 11, Pp 74-81 (2024)
【Objective】 The Huaibei Plain in central China is a major agricultural production area where winter wheat often experiences drought during its growing season. This study analyses the spatiotemporal variation in drought during the winter wheat gro
Externí odkaz:
https://doaj.org/article/90c404d9081d46259ab31b3973656dc0
Autor:
JIANG Xinping, WANG Qimeng, LIU Meng, WANG Faxin, LYU Haishen, CHEN Yu, LI Jie, WANG Zhenlong
Publikováno v:
Guan'gai paishui xuebao, Vol 43, Iss 2, Pp 54-60 (2024)
【Objective】 Soil temperature is not only important for hydrological processes but also plays an imperative role in crop growth and soil biochemical reactions. Understanding its spatiotemporal variation is crucial to improving soil and hydrologica
Externí odkaz:
https://doaj.org/article/75a10974736644978a7495c20f124722
Autor:
Zhang, Chen, Zhong, Meizhi, Wang, Qimeng, Lu, Xuantao, Ye, Zheyu, Lu, Chengqiang, Gao, Yan, Hu, Yao, Chen, Kehai, Zhang, Min, Song, Dawei
Long-context efficiency has recently become a trending topic in serving large language models (LLMs). And mixture of depths (MoD) is proposed as a perfect fit to bring down both latency and memory. In this paper, however, we discover that MoD can bar
Externí odkaz:
http://arxiv.org/abs/2410.14268
Instructional documents are rich sources of knowledge for completing various tasks, yet their unique challenges in conversational question answering (CQA) have not been thoroughly explored. Existing benchmarks have primarily focused on basic factual
Externí odkaz:
http://arxiv.org/abs/2410.00526
Autor:
Wu, Shiwei, Chen, Joya, Lin, Kevin Qinghong, Wang, Qimeng, Gao, Yan, Xu, Qianli, Xu, Tong, Hu, Yao, Chen, Enhong, Shou, Mike Zheng
A well-known dilemma in large vision-language models (e.g., GPT-4, LLaVA) is that while increasing the number of vision tokens generally enhances visual understanding, it also significantly raises memory and computational costs, especially in long-te
Externí odkaz:
http://arxiv.org/abs/2408.16730
CLIP (Contrastive Language-Image Pretraining) is well-developed for open-vocabulary zero-shot image-level recognition, while its applications in pixel-level tasks are less investigated, where most efforts directly adopt CLIP features without delibera
Externí odkaz:
http://arxiv.org/abs/2304.06957
Non-maximum suppression (NMS) is widely used in object detection pipelines for removing duplicated bounding boxes. The inconsistency between the confidence for NMS and the real localization confidence seriously affects detection performance. Prior wo
Externí odkaz:
http://arxiv.org/abs/2202.00866
Autor:
Xu, Yingying, Lü, Haishen, Yagci, Ali Levent, Zhu, Yonghua, Liu, Di, Wang, Qimeng, Xu, Haiting, Pan, Ying, Su, Jianbin
Publikováno v:
In Agricultural Water Management 1 December 2024 305
Temporal action detection (TAD) aims to determine the semantic label and the temporal interval of every action instance in an untrimmed video. It is a fundamental and challenging task in video understanding. Previous methods tackle this task with com
Externí odkaz:
http://arxiv.org/abs/2106.10271
Autor:
Wang, Qimeng1 (AUTHOR), Wu, Jianping1 (AUTHOR), Wang, Mingming2 (AUTHOR), Yu, Haizhou3 (AUTHOR), Qiu, Xiaoyan1 (AUTHOR) iamxyqiu@njtech.edu.cn, Chen, Wei2 (AUTHOR) weichen1@ustc.edu.cn
Publikováno v:
Advanced Science. 3/20/2024, Vol. 11 Issue 11, p1-8. 8p.