Zobrazeno 1 - 10
of 16 458
pro vyhledávání: '"A, Wenwei"'
Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D-awareness
Externí odkaz:
http://arxiv.org/abs/2409.18125
A major limitation of minimally invasive surgery is the difficulty in accurately locating the internal anatomical structures of the target organ due to the lack of tactile feedback and transparency. Augmented reality (AR) offers a promising solution
Externí odkaz:
http://arxiv.org/abs/2409.11688
Spiking neural networks (SNNs) have received widespread attention as an ultra-low energy computing paradigm. Recent studies have focused on improving the feature extraction capability of SNNs, but they suffer from inefficient inference and suboptimal
Externí odkaz:
http://arxiv.org/abs/2408.09108
The pre-training cost of large language models (LLMs) is prohibitive. One cutting-edge approach to reduce the cost is zero-shot weight transfer, also known as model growth for some cases, which magically transfers the weights trained in a small model
Externí odkaz:
http://arxiv.org/abs/2408.08681
Developers use logging statements to monitor software, but misleading logs can complicate maintenance by obscuring actual activities. Existing research on logging quality issues is limited, mainly focusing on single defects and manual fixes. To addre
Externí odkaz:
http://arxiv.org/abs/2408.03101
Autor:
Chen, Zehui, Liu, Kuikun, Wang, Qiuchen, Liu, Jiangning, Zhang, Wenwei, Chen, Kai, Zhao, Feng
Information seeking and integration is a complex cognitive task that consumes enormous time and effort. Inspired by the remarkable progress of Large Language Models, recent works attempt to solve this task by combining LLMs and search engines. Howeve
Externí odkaz:
http://arxiv.org/abs/2407.20183
Autor:
Zhang, Songyang, Zhang, Chuyu, Hu, Yingfan, Shen, Haowen, Liu, Kuikun, Ma, Zerun, Zhou, Fengzhe, Zhang, Wenwei, He, Xuming, Lin, Dahua, Chen, Kai
While LLM-Based agents, which use external tools to solve complex problems, have made significant progress, benchmarking their ability is challenging, thereby hindering a clear understanding of their limitations. In this paper, we propose an interact
Externí odkaz:
http://arxiv.org/abs/2407.10499
Autor:
Xu, Xiang, Kong, Lingdong, Shuai, Hui, Zhang, Wenwei, Pan, Liang, Chen, Kai, Liu, Ziwei, Liu, Qingshan
In the realm of autonomous driving, accurate 3D perception is the foundation. However, developing such models relies on extensive human annotations -- a process that is both costly and labor-intensive. To address this challenge from a data representa
Externí odkaz:
http://arxiv.org/abs/2407.06190
Recent advancements in both transformer-based methods and spiral neighbor sampling techniques have greatly enhanced hand mesh reconstruction. Transformers excel in capturing complex vertex relationships, and spiral neighbor sampling is vital for util
Externí odkaz:
http://arxiv.org/abs/2407.05967
Large language models (LLMs) exhibit hallucinations in long-form question-answering tasks across various domains and wide applications. Current hallucination detection and mitigation datasets are limited in domains and sizes, which struggle to scale
Externí odkaz:
http://arxiv.org/abs/2407.04693