Zobrazeno 1 - 10
of 2 103
pro vyhledávání: '"Liu, YuTong"'
Autor:
Zhang, Xuying, Liu, Yutong, Li, Yangguang, Zhang, Renrui, Liu, Yufei, Wang, Kai, Ouyang, Wanli, Xiong, Zhiwei, Gao, Peng, Hou, Qibin, Cheng, Ming-Ming
We present TAR3D, a novel framework that consists of a 3D-aware Vector Quantized-Variational AutoEncoder (VQ-VAE) and a Generative Pre-trained Transformer (GPT) to generate high-quality 3D assets. The core insight of this work is to migrate the multi
Externí odkaz:
http://arxiv.org/abs/2412.16919
Autor:
Wang, Jingkai, Gong, Jue, Zhang, Lin, Chen, Zheng, Liu, Xing, Gu, Hong, Liu, Yutong, Zhang, Yulun, Yang, Xiaokang
Diffusion models have demonstrated impressive performance in face restoration. Yet, their multi-step inference process remains computationally intensive, limiting their applicability in real-world scenarios. Moreover, existing methods often struggle
Externí odkaz:
http://arxiv.org/abs/2411.17163
For the diagnosis of diabetes retinopathy (DR) images, this paper proposes a classification method based on artificial intelligence. The core lies in a new data augmentation method, GreenBen, which first extracts the green channel grayscale image fro
Externí odkaz:
http://arxiv.org/abs/2410.09444
Autor:
Hou, Chang, Marra, Luigi, Maceda, Guy Y. Cornejo, Jiang, Peng, Chen, Jingguo, Liu, Yutong, Hu, Gang, Chen, Jialong, Ianiro, Andrea, Discetti, Stefano, Meilán-Vila, Andrea, Noack, Bernd R.
We propose a physics-informed data-driven framework for urban wind estimation. This framework validates and incorporates the Reynolds number independence for flows under various working conditions, thus allowing the extrapolation for wind conditions
Externí odkaz:
http://arxiv.org/abs/2410.02427
Autor:
Nagar, Aishik, Liu, Yutong, Liu, Andy T., Schlegel, Viktor, Dwivedi, Vijay Prakash, Kaliya-Perumal, Arun-Kumar, Kalanchiam, Guna Pratheep, Tang, Yili, Tan, Robby T.
Medical abstractive summarization faces the challenge of balancing faithfulness and informativeness. Current methods often sacrifice key information for faithfulness or introduce confabulations when prioritizing informativeness. While recent advancem
Externí odkaz:
http://arxiv.org/abs/2408.12095
Light fields (LFs), conducive to comprehensive scene radiance recorded across angular dimensions, find wide applications in 3D reconstruction, virtual reality, and computational photography.However, the LF acquisition is inevitably time-consuming and
Externí odkaz:
http://arxiv.org/abs/2402.00575
Autor:
Li, Jiakang, Lai, Songning, Shuai, Zhihao, Tan, Yuan, Jia, Yifan, Yu, Mianyang, Song, Zichen, Peng, Xiaokang, Xu, Ziyang, Ni, Yongxin, Qiu, Haifeng, Yang, Jiayu, Liu, Yutong, Lu, Yonggang
The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. Detecting communities in graphs is a challenging problem with applications in sociology, biolog
Externí odkaz:
http://arxiv.org/abs/2309.11798
Experimental assessment of safe and precise flight control algorithms for unmanned aerial vehicles (UAVs) under gusty wind conditions requires the capability to generate a large range of velocity profiles. In this study, we employ a small fan array w
Externí odkaz:
http://arxiv.org/abs/2309.00458
Deep learning has opened up new possibilities for light field super-resolution (SR), but existing methods trained on synthetic datasets with simple degradations (e.g., bicubic downsampling) suffer from poor performance when applied to complex real-wo
Externí odkaz:
http://arxiv.org/abs/2305.18994
Autor:
Lai, Songning, Li, Jiakang, Guo, Guinan, Hu, Xifeng, Li, Yulong, Tan, Yuan, Song, Zichen, Liu, Yutong, Ren, Zhaoxia, Wan, Chun, Miao, Danmin, Liu, Zhi
Publikováno v:
International Joint Conference on Neural Networks (IJCNN) 2024
Designing an effective representation learning method for multimodal sentiment analysis tasks is a crucial research direction. The challenge lies in learning both shared and private information in a complete modal representation, which is difficult w
Externí odkaz:
http://arxiv.org/abs/2305.08473