Zobrazeno 1 - 10
of 23 098
pro vyhledávání: '"Yang, Cheng"'
Autor:
Fox, Elizabeth L., Cook, Ashley D., Yang, Cheng-Ta, Fu, Hao-Lun, Latthirun, Kanthika, Howard, Zachary L.
Publikováno v:
Tutorials in Quantitative Methods for Psychology, Vol 19, Iss 1, Pp 84-99 (2023)
In high demand contexts, uni- or multi-modal signals are used to convey redundant information and improve performance. This is especially the case with improving the detection of discrete peripheral signals. However, how one processes peripheral sign
Externí odkaz:
https://doaj.org/article/a8597632205643779c1f3e975ce833a2
Unsupervised Domain Adaptive Object Detection (DAOD) could adapt a model trained on a source domain to an unlabeled target domain for object detection. Existing unsupervised DAOD methods usually perform feature alignments from the target to the sourc
Externí odkaz:
http://arxiv.org/abs/2407.02835
Autor:
Li, Cheng-Yi, Chang, Kao-Jung, Yang, Cheng-Fu, Wu, Hsin-Yu, Chen, Wenting, Bansal, Hritik, Chen, Ling, Yang, Yi-Ping, Chen, Yu-Chun, Chen, Shih-Pin, Lirng, Jiing-Feng, Chang, Kai-Wei, Chiou, Shih-Hwa
Multi-modal large language models (MLLMs) have been given free rein to explore exciting medical applications with a primary focus on radiology report generation. Nevertheless, the preliminary success in 2D radiology captioning is incompetent to refle
Externí odkaz:
http://arxiv.org/abs/2407.02235
Multi-view counting (MVC) methods have shown their superiority over single-view counterparts, particularly in situations characterized by heavy occlusion and severe perspective distortions. However, hand-crafted heuristic features and identical camer
Externí odkaz:
http://arxiv.org/abs/2407.02047
Autor:
Zhang, Yuxiang, Chen, Jing, Wang, Junjie, Liu, Yaxin, Yang, Cheng, Shi, Chufan, Zhu, Xinyu, Lin, Zihao, Wan, Hanwen, Yang, Yujiu, Sakai, Tetsuya, Feng, Tian, Yamana, Hayato
Tool-augmented large language models (LLMs) are rapidly being integrated into real-world applications. Due to the lack of benchmarks, the community still needs to fully understand the hallucination issues within these models. To address this challeng
Externí odkaz:
http://arxiv.org/abs/2406.20015
Drift is a significant issue that undermines the reliability of gas sensors. This paper introduces a probabilistic model to distinguish between environmental variation and instrumental drift, using low-cost non-dispersive infrared (NDIR) CO2 sensors
Externí odkaz:
http://arxiv.org/abs/2406.17488
In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A
Externí odkaz:
http://arxiv.org/abs/2406.15784
Autor:
Liu, Wei, Wang, Chenxi, Wang, Yifei, Xie, Zihao, Qiu, Rennai, Dang, Yufan, Du, Zhuoyun, Chen, Weize, Yang, Cheng, Qian, Chen
Large Language Model Multi-Agent Systems (LLM-MAS) have achieved great progress in solving complex tasks. It performs communication among agents within the system to collaboratively solve tasks, under the premise of shared information. However, when
Externí odkaz:
http://arxiv.org/abs/2406.14928
Path planning is a fundamental scientific problem in robotics and autonomous navigation, requiring the derivation of efficient routes from starting to destination points while avoiding obstacles. Traditional algorithms like A* and its variants are ca
Externí odkaz:
http://arxiv.org/abs/2407.02511
Self-consistent strong plasma screening around light nuclei is implemented in the Big Bang nucleosynthesis (BBN) epoch to determine the short-range screening potential, $e\phi(r)/T \geq 1$, relevant for thermonuclear reactions. We numerically solve t
Externí odkaz:
http://arxiv.org/abs/2406.13055