Zobrazeno 1 - 10
of 36 956
pro vyhledávání: '"a Lin Wang"'
Autor:
Ge, Zhiqi, Li, Juncheng, Pang, Xinglei, Gao, Minghe, Pan, Kaihang, Lin, Wang, Fei, Hao, Zhang, Wenqiao, Tang, Siliang, Zhuang, Yueting
Digital agents are increasingly employed to automate tasks in interactive digital environments such as web pages, software applications, and operating systems. While text-based agents built on Large Language Models (LLMs) often require frequent updat
Externí odkaz:
http://arxiv.org/abs/2412.10342
Multimodal sentiment analysis (MSA) is an emerging research topic that aims to understand and recognize human sentiment or emotions through multiple modalities. However, in real-world dynamic scenarios, the distribution of target data is always chang
Externí odkaz:
http://arxiv.org/abs/2412.07121
Neural network force field models such as DeePMD have enabled highly efficient large-scale molecular dynamics simulations with ab initio accuracy. However, building such models heavily depends on the training data obtained by costly electronic struct
Externí odkaz:
http://arxiv.org/abs/2411.13850
With the rapid advancement of large language models, there has been a growing interest in their capabilities in mathematical reasoning. However, existing research has primarily focused on text-based algebra problems, neglecting the study of geometry
Externí odkaz:
http://arxiv.org/abs/2409.09039
Autor:
Wu, Tao, Li, Mengze, Chen, Jingyuan, Ji, Wei, Lin, Wang, Gao, Jinyang, Kuang, Kun, Zhao, Zhou, Wu, Fei
Research on Multi-modal Large Language Models (MLLMs) towards the multi-image cross-modal instruction has received increasing attention and made significant progress, particularly in scenarios involving closely resembling images (e.g., change caption
Externí odkaz:
http://arxiv.org/abs/2408.12867
As the open community of large language models (LLMs) matures, multimodal LLMs (MLLMs) have promised an elegant bridge between vision and language. However, current research is inherently constrained by challenges such as the need for high-quality in
Externí odkaz:
http://arxiv.org/abs/2408.05019
Recent advances in text-to-3D generation have made significant progress. In particular, with the pretrained diffusion models, existing methods predominantly use Score Distillation Sampling (SDS) to train 3D models such as Neural RaRecent advances in
Externí odkaz:
http://arxiv.org/abs/2408.05008
Autor:
Wang, Ye, Xun, Jiahao, Hong, Minjie, Zhu, Jieming, Jin, Tao, Lin, Wang, Li, Haoyuan, Li, Linjun, Xia, Yan, Zhao, Zhou, Dong, Zhenhua
Generative retrieval has recently emerged as a promising approach to sequential recommendation, framing candidate item retrieval as an autoregressive sequence generation problem. However, existing generative methods typically focus solely on either b
Externí odkaz:
http://arxiv.org/abs/2406.14017
Autor:
Specht, Petra, Kang, Joo H., Tarafder, Kartick, Cieslinski, Robert, Barton, David, Barton, Bastian, Carlsson, Anna, Wang, Lin-Wang, Kisielowski, Christian
The static and genuine structure of small rhodium and rhodium/tungsten nanoparticles on an alumina support can be imaged with atomic resolution even if single digit atom clusters are investigated. Low dose rate electron microscopy is key to the achie
Externí odkaz:
http://arxiv.org/abs/2406.05689
Autor:
Lin, Wang, Chen, Jingyuan, Shi, Jiaxin, Zhu, Yichen, Liang, Chen, Miao, Junzhong, Jin, Tao, Zhao, Zhou, Wu, Fei, Yan, Shuicheng, Zhang, Hanwang
We tackle the common challenge of inter-concept visual confusion in compositional concept generation using text-guided diffusion models (TGDMs). It becomes even more pronounced in the generation of customized concepts, due to the scarcity of user-pro
Externí odkaz:
http://arxiv.org/abs/2405.06914