Zobrazeno 1 - 10
of 318
pro vyhledávání: '"Zhang Rongsheng"'
This paper investigates deep learning enabled beamforming design for ultra-dense wireless networks by integrating prior knowledge and graph neural network (GNN), named model-based GNN. A energy efficiency (EE) maximization problem is formulated subje
Externí odkaz:
http://arxiv.org/abs/2410.02289
Autor:
Ma, Yuhang, Xu, Wenting, Tang, Jiji, Jin, Qinfeng, Zhang, Rongsheng, Zhao, Zeng, Fan, Changjie, Hu, Zhipeng
Customized image generation, which seeks to synthesize images with consistent characters, holds significant relevance for applications such as storytelling, portrait generation, and character design. However, previous approaches have encountered chal
Externí odkaz:
http://arxiv.org/abs/2406.16537
Autor:
Chen, Jing, Zhu, Xinyu, Yang, Cheng, Shi, Chufan, Xi, Yadong, Zhang, Yuxiang, Wang, Junjie, Pu, Jiashu, Zhang, Rongsheng, Yang, Yujiu, Feng, Tian
Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in natural language processing. In particular, large language models (LLMs) can hardly produce written works at the lev
Externí odkaz:
http://arxiv.org/abs/2406.11683
Dynamic Vehicle Routing Problem (DVRP), is an extension of the classic Vehicle Routing Problem (VRP), which is a fundamental problem in logistics and transportation. Typically, DVRPs involve two stakeholders: service providers that deliver services t
Externí odkaz:
http://arxiv.org/abs/2405.19184
Autor:
Zang, Chuanqi, Tang, Jiji, Zhang, Rongsheng, Zhao, Zeng, Lv, Tangjie, Pei, Mingtao, Liang, Wei
Storytelling aims to generate reasonable and vivid narratives based on an ordered image stream. The fidelity to the image story theme and the divergence of story plots attract readers to keep reading. Previous works iteratively improved the alignment
Externí odkaz:
http://arxiv.org/abs/2403.07301
Autor:
Pu, Jiashu, Wan, Yajing, Zhang, Yuru, Chen, Jing, Cheng, Ling, Shao, Qian, Chang, Yongzhu, Lv, Tangjie, Zhang, Rongsheng
Previous in-context learning (ICL) research has focused on tasks such as classification, machine translation, text2table, etc., while studies on whether ICL can improve human-like dialogue generation are scarce. Our work fills this gap by systematica
Externí odkaz:
http://arxiv.org/abs/2402.09954
Autor:
Zou, Siyu, Tang, Jiji, Zhou, Yiyi, He, Jing, Zhao, Chaoyi, Zhang, Rongsheng, Hu, Zhipeng, Sun, Xiaoshuai
Diffusion-based Image Editing (DIE) is an emerging research hot-spot, which often applies a semantic mask to control the target area for diffusion-based editing. However, most existing solutions obtain these masks via manual operations or off-line pr
Externí odkaz:
http://arxiv.org/abs/2401.07709
Autor:
Pu, Jiashu, Zhao, Shiwei, Cheng, Ling, Chang, Yongzhu, Wu, Runze, Lv, Tangjie, Zhang, Rongsheng
Although the pre-training followed by fine-tuning paradigm is used extensively in many fields, there is still some controversy surrounding the impact of pre-training on the fine-tuning process. Currently, experimental findings based on text and image
Externí odkaz:
http://arxiv.org/abs/2309.05256
Publikováno v:
publish EMNLP 2023
Lyrics generation is a well-known application in natural language generation research, with several previous studies focusing on generating accurate lyrics using precise control such as keywords, rhymes, etc. However, lyrics imitation, which involves
Externí odkaz:
http://arxiv.org/abs/2308.04665
Publikováno v:
published ICDAR 2023 D-NLP
Simile detection is a valuable task for many natural language processing (NLP)-based applications, particularly in the field of literature. However, existing research on simile detection often relies on corpora that are limited in size and do not ade
Externí odkaz:
http://arxiv.org/abs/2308.04109