Zobrazeno 1 - 10
of 8 561
pro vyhledávání: '"Peng, Tao"'
Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI's GPT family, have proven to be very helpful for many natural language processing (NLP) related tasks. Therefore, various attempts have been made to appl
Externí odkaz:
http://arxiv.org/abs/2409.00369
Autor:
Jin, Rui, Li, Derun, Xiang, Dehui, Zhang, Lei, Zhou, Hailing, Shi, Fei, Zhu, Weifang, Cai, Jing, Peng, Tao, Chen, Xinjian
Prostate cancer represents a major threat to health. Early detection is vital in reducing the mortality rate among prostate cancer patients. One approach involves using multi-modality (CT, MRI, US, etc.) computer-aided diagnosis (CAD) systems for the
Externí odkaz:
http://arxiv.org/abs/2407.06612
Foundational models have significantly advanced in natural language processing (NLP) and computer vision (CV), with the Transformer architecture becoming a standard backbone. However, the Transformer's quadratic complexity poses challenges for handli
Externí odkaz:
http://arxiv.org/abs/2405.14480
The task of steel surface defect recognition is an industrial problem with great industry values. The data insufficiency is the major challenge in training a robust defect recognition network. Existing methods have investigated to enlarge the dataset
Externí odkaz:
http://arxiv.org/abs/2405.01872
Previous multi-task dense prediction methods based on the Mixture of Experts (MoE) have received great performance but they neglect the importance of explicitly modeling the global relations among all tasks. In this paper, we present a novel decoder-
Externí odkaz:
http://arxiv.org/abs/2403.17749
Multi-modal large language models (MLLMs) can understand image-language prompts and demonstrate impressive reasoning ability. In this paper, we extend MLLMs' output by empowering MLLMs with the segmentation ability. The extended MLLMs can both output
Externí odkaz:
http://arxiv.org/abs/2403.14141
Generative models have shown strong generation ability while efficient likelihood estimation is less explored. Energy-based models~(EBMs) define a flexible energy function to parameterize unnormalized densities efficiently but are notorious for being
Externí odkaz:
http://arxiv.org/abs/2403.01666
In recent years, the rapid development of deep learning technology has brought new prospects to the field of vulnerability detection. Many vulnerability detection methods involve converting source code into images for detection, yet they often overlo
Externí odkaz:
http://arxiv.org/abs/2402.18189
Autor:
Lin, Bin, Zhang, Chen, Peng, Tao, Zhao, Hanyu, Xiao, Wencong, Sun, Minmin, Liu, Anmin, Zhang, Zhipeng, Li, Lanbo, Qiu, Xiafei, Li, Shen, Ji, Zhigang, Xie, Tao, Li, Yong, Lin, Wei
Large Language Models (LLMs) demonstrate substantial potential across a diverse array of domains via request serving. However, as trends continue to push for expanding context sizes, the autoregressive nature of LLMs results in highly dynamic behavio
Externí odkaz:
http://arxiv.org/abs/2401.02669
Adverse weather image restoration strives to recover clear images from those affected by various weather types, such as rain, haze, and snow. Each weather type calls for a tailored degradation removal approach due to its unique impact on images. Conv
Externí odkaz:
http://arxiv.org/abs/2312.05006