Zobrazeno 1 - 10
of 13 475
pro vyhledávání: '"Hung, Yu"'
Autor:
Chao, Brian, Tseng, Hung-Yu, Porzi, Lorenzo, Gao, Chen, Li, Tuotuo, Li, Qinbo, Saraf, Ayush, Huang, Jia-Bin, Kopf, Johannes, Wetzstein, Gordon, Kim, Changil
3D Gaussian Splatting (3DGS) has recently emerged as a state-of-the-art 3D reconstruction and rendering technique due to its high-quality results and fast training and rendering time. However, pixels covered by the same Gaussian are always shaded in
Externí odkaz:
http://arxiv.org/abs/2411.18625
Autor:
Hung, Yu-Kai, Huang, Yun-Chien, Su, Ting-Yu, Lin, Yen-Ting, Cheng, Lung-Pan, Wang, Bryan, Sun, Shao-Hua
Audience feedback is crucial for refining video content, yet it typically comes after publication, limiting creators' ability to make timely adjustments. To bridge this gap, we introduce SimTube, a generative AI system designed to simulate audience f
Externí odkaz:
http://arxiv.org/abs/2411.09577
Explainable Recommendation task is designed to receive a pair of user and item and output explanations to justify why an item is recommended to a user. Many models treat review-generation as a proxy of explainable recommendation. Although they are ab
Externí odkaz:
http://arxiv.org/abs/2408.09865
Autor:
Lin, Chieh Hubert, Kim, Changil, Huang, Jia-Bin, Li, Qinbo, Ma, Chih-Yao, Kopf, Johannes, Yang, Ming-Hsuan, Tseng, Hung-Yu
Neural Radiance Field (NeRF) is a representation for 3D reconstruction from multi-view images. Despite some recent work showing preliminary success in editing a reconstructed NeRF with diffusion prior, they remain struggling to synthesize reasonable
Externí odkaz:
http://arxiv.org/abs/2404.09995
Autor:
Höllein, Lukas, Božič, Aljaž, Müller, Norman, Novotny, David, Tseng, Hung-Yu, Richardt, Christian, Zollhöfer, Michael, Nießner, Matthias
3D asset generation is getting massive amounts of attention, inspired by the recent success of text-guided 2D content creation. Existing text-to-3D methods use pretrained text-to-image diffusion models in an optimization problem or fine-tune them on
Externí odkaz:
http://arxiv.org/abs/2403.01807
Autor:
Lin, Ying-Jia, Lin, Chun-Yi, Yeh, Chia-Jen, Li, Yi-Ting, Hu, Yun-Yu, Hsu, Chih-Hao, Lee, Mei-Feng, Kao, Hung-Yu
We present CFEVER, a Chinese dataset designed for Fact Extraction and VERification. CFEVER comprises 30,012 manually created claims based on content in Chinese Wikipedia. Each claim in CFEVER is labeled as "Supports", "Refutes", or "Not Enough Info"
Externí odkaz:
http://arxiv.org/abs/2402.13025
Publikováno v:
Pacific Accounting Review, 2024, Vol. 36, Issue 3/4, pp. 468-489.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/PAR-12-2023-0169
Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate due to the immitigable domain gap. We introduce DMP, a pipeline utilizing pre-trained T2I
Externí odkaz:
http://arxiv.org/abs/2311.18832
Autor:
AlBahar, Badour, Saito, Shunsuke, Tseng, Hung-Yu, Kim, Changil, Kopf, Johannes, Huang, Jia-Bin
We present an approach to generate a 360-degree view of a person with a consistent, high-resolution appearance from a single input image. NeRF and its variants typically require videos or images from different viewpoints. Most existing approaches tak
Externí odkaz:
http://arxiv.org/abs/2311.09221
We consider the infinite-horizon linear Markov Decision Processes (MDPs), where the transition probabilities of the dynamic model can be linearly parameterized with the help of a predefined low-dimensional feature mapping. While the existing regressi
Externí odkaz:
http://arxiv.org/abs/2310.11515