Zobrazeno 1 - 10
of 468
pro vyhledávání: '"Wang, ZhaoQing"'
Recent advancements in Virtual Try-On (VTO) have demonstrated exceptional efficacy in generating realistic images and preserving garment details, largely attributed to the robust generative capabilities of text-to-image (T2I) diffusion backbones. How
Externí odkaz:
http://arxiv.org/abs/2411.17017
Autor:
Chen, Haodong, Chen, Runnan, Qu, Qiang, Wang, Zhaoqing, Liu, Tongliang, Chen, Xiaoming, Chung, Yuk Ying
Recent advancements in 3D Gaussian Splatting (3DGS) have substantially improved novel view synthesis, enabling high-quality reconstruction and real-time rendering. However, blurring artifacts, such as floating primitives and over-reconstruction, rema
Externí odkaz:
http://arxiv.org/abs/2411.12440
Autor:
Wang, Zhaoqing, Xia, Xiaobo, Chen, Runnan, Yu, Dongdong, Wang, Changhu, Gong, Mingming, Liu, Tongliang
This paper presents the Large Vision Diffusion Transformer (LaVin-DiT), a scalable and unified foundation model designed to tackle over 20 computer vision tasks in a generative framework. Unlike existing large vision models directly adapted from natu
Externí odkaz:
http://arxiv.org/abs/2411.11505
Interactive video object segmentation is a crucial video task, having various applications from video editing to data annotating. However, current approaches struggle to accurately segment objects across diverse domains. Recently, Segment Anything Mo
Externí odkaz:
http://arxiv.org/abs/2406.05485
Autor:
Wang, Zhaoqing, Xia, Xiaobo, Chen, Ziye, He, Xiao, Guo, Yandong, Gong, Mingming, Liu, Tongliang
Current state-of-the-art open-vocabulary segmentation methods typically rely on image-mask-text triplet annotations for supervision. However, acquiring such detailed annotations is labour-intensive and poses scalability challenges in complex real-wor
Externí odkaz:
http://arxiv.org/abs/2402.08960
Autor:
Zhang, Shaokun, Xia, Xiaobo, Wang, Zhaoqing, Chen, Ling-Hao, Liu, Jiale, Wu, Qingyun, Liu, Tongliang
In-context learning is a promising paradigm that utilizes in-context examples as prompts for the predictions of large language models. These prompts are crucial for achieving strong performance. However, since the prompts need to be sampled from a la
Externí odkaz:
http://arxiv.org/abs/2310.10873
Publikováno v:
IET Generation, Transmission & Distribution, Vol 18, Iss 21, Pp 3501-3509 (2024)
Abstract The complexity of power quality (PQ) concerns is intensifying in tandem with the proliferation of inverter‐based renewable energy systems. The integration of power electronic devices within the distribution network exacerbates the complexi
Externí odkaz:
https://doaj.org/article/6c30246b2f5347b0b7255b76a4c524a6
Autor:
Chi, Xiaowei, Liu, Jiaming, Lu, Ming, Zhang, Rongyu, Wang, Zhaoqing, Guo, Yandong, Zhang, Shanghang
Bird's-Eye-View (BEV) 3D Object Detection is a crucial multi-view technique for autonomous driving systems. Recently, plenty of works are proposed, following a similar paradigm consisting of three essential components, i.e., camera feature extraction
Externí odkaz:
http://arxiv.org/abs/2212.01231
Autor:
Bai, Yingbin, Yang, Erkun, Wang, Zhaoqing, Du, Yuxuan, Han, Bo, Deng, Cheng, Wang, Dadong, Liu, Tongliang
Most recent self-supervised learning methods learn visual representation by contrasting different augmented views of images. Compared with supervised learning, more aggressive augmentations have been introduced to further improve the diversity of tra
Externí odkaz:
http://arxiv.org/abs/2206.01999
Autor:
Kang, Qiangqiang, Zhang, Yulan, Kang, Shichang, Gao, Tanguang, Zhao, Yujiao, Luo, Xi, Guo, Junming, Wang, Zhaoqing, Zhang, Shuncun
Publikováno v:
In Environmental Pollution 15 December 2024 363 Part 1