Zobrazeno 1 - 10
of 7 284
pro vyhledávání: '"Li, Yiming"'
Vision-based 3D occupancy prediction is significantly challenged by the inherent limitations of monocular vision in depth estimation. This paper introduces CVT-Occ, a novel approach that leverages temporal fusion through the geometric correspondence
Externí odkaz:
http://arxiv.org/abs/2409.13430
Autor:
Zhao, Yuxiang, Hu, Jiangyong, Liu, Ruijuan, Gao, Ruochen, Li, Yiming, Zhang, Xiao, Zhu, Huanfeng, Wu, Saijun
Acousto-optical modulation (AOM) is a powerful and widely used technique for rapidly controlling the frequency, phase, intensity, and direction of light. Based on Bragg diffraction, AOMs typically exhibit moderate diffraction efficiency, often less t
Externí odkaz:
http://arxiv.org/abs/2408.15051
Recent advances have been witnessed in audio-language joint learning, such as CLAP, that shows much success in multi-modal understanding tasks. These models usually aggregate uni-modal local representations, namely frame or word features, into global
Externí odkaz:
http://arxiv.org/abs/2408.07919
Recently, point clouds have been widely used in computer vision, whereas their collection is time-consuming and expensive. As such, point cloud datasets are the valuable intellectual property of their owners and deserve protection. To detect and prev
Externí odkaz:
http://arxiv.org/abs/2408.05500
Although the widespread use of AI systems in today's world is growing, many current AI systems are found vulnerable due to hidden bias and missing information, especially in the most commonly used forecasting system. In this work, we explore the robu
Externí odkaz:
http://arxiv.org/abs/2407.19688
Autor:
Li, Yiming, Zong, Chuanming
In 1694, Gregory and Newton proposed the problem to determine the kissing number of a rigid material ball. This problem and its higher dimensional generalization have been studied by many mathematicians, including Minkowski, van der Waerden, Hadwiger
Externí odkaz:
http://arxiv.org/abs/2407.17340
Autor:
Yang, Yuchen, Yao, Hongwei, Yang, Bingrun, He, Yiling, Li, Yiming, Zhang, Tianwei, Qin, Zhan, Ren, Kui
Recently, code-oriented large language models (Code LLMs) have been widely and successfully used to simplify and facilitate code programming. With these tools, developers can easily generate desired complete functional codes based on incomplete code
Externí odkaz:
http://arxiv.org/abs/2407.09164
Large language models (LLMs) exhibit a variety of promising capabilities in robotics, including long-horizon planning and commonsense reasoning. However, their performance in place recognition is still underexplored. In this work, we introduce multim
Externí odkaz:
http://arxiv.org/abs/2406.17520
Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations. Traditional deep learning models are adept at learning intricate feature representations and depend
Externí odkaz:
http://arxiv.org/abs/2406.18049
Autor:
Li, Yiming, Li, Zhiheng, Chen, Nuo, Gong, Moonjun, Lyu, Zonglin, Wang, Zehong, Jiang, Peili, Feng, Chen
Large-scale datasets have fueled recent advancements in AI-based autonomous vehicle research. However, these datasets are usually collected from a single vehicle's one-time pass of a certain location, lacking multiagent interactions or repeated trave
Externí odkaz:
http://arxiv.org/abs/2406.09383