Zobrazeno 1 - 10
of 40 290
pro vyhledávání: '"Xiao, Jun‐An"'
Blind image restoration remains a significant challenge in low-level vision tasks. Recently, denoising diffusion models have shown remarkable performance in image synthesis. Guided diffusion models, leveraging the potent generative priors of pre-trai
Externí odkaz:
http://arxiv.org/abs/2411.12450
Euclidean representation learning methods have achieved commendable results in image fusion tasks, which can be attributed to their clear advantages in handling with linear space. However, data collected from a realistic scene usually have a non-Eucl
Externí odkaz:
http://arxiv.org/abs/2411.10679
Autor:
Zuo, Yushen, Xiao, Jun, Chan, Kin-Chung, Dong, Rongkang, Yang, Cuixin, He, Zongqi, Xie, Hao, Lam, Kin-Man
The stylization of 3D scenes is an increasingly attractive topic in 3D vision. Although image style transfer has been extensively researched with promising results, directly applying 2D style transfer methods to 3D scenes often fails to preserve the
Externí odkaz:
http://arxiv.org/abs/2411.10130
Autor:
Gao, Xiao-Jun
In this paper, we investigate the strong gravitational lensing effect around a spherically symmetric regular black hole, whose metric is derived from a non-singular collapsing dust ball model in asymptotically safe gravity. In this regular black hole
Externí odkaz:
http://arxiv.org/abs/2411.09513
Superluminal propagation is an intrinsic problem in the diffusion equation and has not been effectively addressed for a long time. In this work, a rigorous solution to this issue is obtained under the assumption that particles undergo a random flight
Externí odkaz:
http://arxiv.org/abs/2410.19396
Image style transfer has attracted widespread attention in the past few years. Despite its remarkable results, it requires additional style images available as references, making it less flexible and inconvenient. Using text is the most natural way t
Externí odkaz:
http://arxiv.org/abs/2410.09566
Customized Image Generation, generating customized images with user-specified concepts, has raised significant attention due to its creativity and novelty. With impressive progress achieved in subject customization, some pioneer works further explore
Externí odkaz:
http://arxiv.org/abs/2410.02483
Autor:
Miao, Bingchen, Zhang, Wenqiao, Li, Juncheng, Tang, Siliang, Li, Zhaocheng, Shi, Haochen, Xiao, Jun, Zhuang, Yueting
Multimodal Industrial Anomaly Detection (MIAD), utilizing 3D point clouds and 2D RGB images to identify the abnormal region of products, plays a crucial role in industrial quality inspection. However, the conventional MIAD setting presupposes that al
Externí odkaz:
http://arxiv.org/abs/2410.01737
Recent advances in Vision-Language Models (VLMs) and the scarcity of high-quality multi-modal alignment data have inspired numerous researches on synthetic VLM data generation. The conventional norm in VLM data construction uses a mixture of speciali
Externí odkaz:
http://arxiv.org/abs/2409.20424
Autor:
Nazarczuk, Michal, Catley-Chandar, Sibi, Tanay, Thomas, Shaw, Richard, Pérez-Pellitero, Eduardo, Timofte, Radu, Yan, Xing, Wang, Pan, Guo, Yali, Wu, Yongxin, Cai, Youcheng, Yang, Yanan, Li, Junting, Zhou, Yanghong, Mok, P. Y., He, Zongqi, Xiao, Zhe, Chan, Kin-Chung, Goshu, Hana Lebeta, Yang, Cuixin, Dong, Rongkang, Xiao, Jun, Lam, Kin-Man, Hao, Jiayao, Gao, Qiong, Zu, Yanyan, Zhang, Junpei, Jiao, Licheng, Liu, Xu, Purohit, Kuldeep
This paper reviews the challenge on Sparse Neural Rendering that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. This manuscript focuses on the competition set-up, the proposed methods and their resp
Externí odkaz:
http://arxiv.org/abs/2409.15045