Zobrazeno 1 - 10
of 2 580
pro vyhledávání: '"ZHANG, Xiaopeng"'
Autor:
Chen, Yabo, Yang, Chen, Fang, Jiemin, Zhang, Xiaopeng, Xie, Lingxi, Shen, Wei, Dai, Wenrui, Xiong, Hongkai, Tian, Qi
Single-image 3D reconstruction remains a fundamental challenge in computer vision due to inherent geometric ambiguities and limited viewpoint information. Recent advances in Latent Video Diffusion Models (LVDMs) offer promising 3D priors learned from
Externí odkaz:
http://arxiv.org/abs/2412.09597
Autor:
Feng, Chen, Zhuo, Shaojie, Zhang, Xiaopeng, Ramakrishnan, Ramchalam Kinattinkara, Yuan, Zhaocong, Li, Andrew Zou
Continuously adapting pre-trained models to local data on resource constrained edge devices is the $\emph{last mile}$ for model deployment. However, as models increase in size and depth, backpropagation requires a large amount of memory, which become
Externí odkaz:
http://arxiv.org/abs/2411.04036
Hallucination is a common problem for Large Vision-Language Models (LVLMs) with long generations which is difficult to eradicate. The generation with hallucinations is partially inconsistent with the image content. To mitigate hallucination, current
Externí odkaz:
http://arxiv.org/abs/2409.16494
Accurate skin lesion segmentation from dermoscopic images is of great importance for skin cancer diagnosis. However, automatic segmentation of melanoma remains a challenging task because it is difficult to incorporate useful texture representations i
Externí odkaz:
http://arxiv.org/abs/2409.08652
Autor:
Chen, Pengfei, Xie, Lingxi, Huo, Xinyue, Yu, Xuehui, Zhang, Xiaopeng, Sun, Yingfei, Han, Zhenjun, Tian, Qi
The Segment Anything model (SAM) has shown a generalized ability to group image pixels into patches, but applying it to semantic-aware segmentation still faces major challenges. This paper presents SAM-CP, a simple approach that establishes two types
Externí odkaz:
http://arxiv.org/abs/2407.16682
Autor:
Yi, Taoran, Fang, Jiemin, Zhou, Zanwei, Wang, Junjie, Wu, Guanjun, Xie, Lingxi, Zhang, Xiaopeng, Liu, Wenyu, Wang, Xinggang, Tian, Qi
Recently, 3D Gaussian splatting (3D-GS) has achieved great success in reconstructing and rendering real-world scenes. To transfer the high rendering quality to generation tasks, a series of research works attempt to generate 3D-Gaussian assets from t
Externí odkaz:
http://arxiv.org/abs/2406.18462
Autor:
Wang, Fangzhou, Wang, Qijing, Alrahis, Lilas, Fu, Bangqi, Jiang, Shui, Zhang, Xiaopeng, Sinanoglu, Ozgur, Ho, Tsung-Yi, Young, Evangeline F. Y., Knechtel, Johann
Due to cost benefits, supply chains of integrated circuits (ICs) are largely outsourced nowadays. However, passing ICs through various third-party providers gives rise to many security threats, like piracy of IC intellectual property or insertion of
Externí odkaz:
http://arxiv.org/abs/2405.05590
Autor:
Ge, Jiannan, Xie, Lingxi, Xie, Hongtao, Li, Pandeng, Zhang, Xiaopeng, Zhang, Yongdong, Tian, Qi
Publikováno v:
ECCV 2024
A serious issue that harms the performance of zero-shot visual recognition is named objective misalignment, i.e., the learning objective prioritizes improving the recognition accuracy of seen classes rather than unseen classes, while the latter is th
Externí odkaz:
http://arxiv.org/abs/2404.05667
Autor:
Yang, Chen, Li, Sikuang, Fang, Jiemin, Liang, Ruofan, Xie, Lingxi, Zhang, Xiaopeng, Shen, Wei, Tian, Qi
Reconstructing and rendering 3D objects from highly sparse views is of critical importance for promoting applications of 3D vision techniques and improving user experience. However, images from sparse views only contain very limited 3D information, l
Externí odkaz:
http://arxiv.org/abs/2402.10259
Autor:
Shi, Bowen, Zhao, Peisen, Wang, Zichen, Zhang, Yuhang, Wang, Yaoming, Li, Jin, Dai, Wenrui, Zou, Junni, Xiong, Hongkai, Tian, Qi, Zhang, Xiaopeng
Vision-language foundation models, represented by Contrastive Language-Image Pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual tasks. However, existing approaches primarily focus on training model
Externí odkaz:
http://arxiv.org/abs/2401.06397