Zobrazeno 1 - 10
of 612
pro vyhledávání: '"Cheng Yuqi"'
Publikováno v:
Open Geosciences, Vol 15, Iss 1, Pp 82-92 (2023)
The Mongolia-Okhotsk tectonic regime had a significant impact on the tectonic evolution of Northeastern (NE) China. However, there is no consensus on the role of this regime in the geological evolution of the Xing’an Massif during the late Mesozoic
Externí odkaz:
https://doaj.org/article/d0d0b1fd5a54488b93b6a2b565d31b41
Detecting anomalies within point clouds is crucial for various industrial applications, but traditional unsupervised methods face challenges due to data acquisition costs, early-stage production constraints, and limited generalization across product
Externí odkaz:
http://arxiv.org/abs/2409.13162
In robotic inspection, joint registration of multiple point clouds is an essential technique for estimating the transformation relationships between measured parts, such as multiple blades in a propeller. However, the presence of noise and outliers i
Externí odkaz:
http://arxiv.org/abs/2409.09682
Autor:
Cao, Yunkang, Zhang, Jiangning, Frittoli, Luca, Cheng, Yuqi, Shen, Weiming, Boracchi, Giacomo
Zero-shot anomaly detection (ZSAD) targets the identification of anomalies within images from arbitrary novel categories. This study introduces AdaCLIP for the ZSAD task, leveraging a pre-trained vision-language model (VLM), CLIP. AdaCLIP incorporate
Externí odkaz:
http://arxiv.org/abs/2407.15795
Robustness against noisy imaging is crucial for practical image anomaly detection systems. This study introduces a Robust Anomaly Detection (RAD) dataset with free views, uneven illuminations, and blurry collections to systematically evaluate the rob
Externí odkaz:
http://arxiv.org/abs/2406.07176
Autor:
Cao, Yunkang, Xu, Xiaohao, Zhang, Jiangning, Cheng, Yuqi, Huang, Xiaonan, Pang, Guansong, Shen, Weiming
Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e.g., industrial defect inspection, and medical lesion detection. This survey comprehensively examine
Externí odkaz:
http://arxiv.org/abs/2401.16402
This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge. Going beyond uni-modal prompt, e.g., language prompt, we present a novel framework, i.e., Se
Externí odkaz:
http://arxiv.org/abs/2306.09067
We present a novel framework, i.e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundation models. Existing anomaly segmentation models typically rely on dom
Externí odkaz:
http://arxiv.org/abs/2305.10724
Autor:
Song, Dingguo, Zhang, Changjun, Cheng, Yuqi, Chen, Linlin, Lin, Jie, Zheng, Changdi, Liu, Ting, Ding, Yuxin, Ling, Fei, Zhong, Weihui
Publikováno v:
In Green Synthesis and Catalysis November 2024 5(4):290-296
Autor:
Yang, Runxu, Wang, Rui, Zhao, Dongyan, Lian, Kun, Shang, Binli, Dong, Lei, Yang, Xuejuan, Dang, Xinglun, Sun, Duo, Cheng, Yuqi
Publikováno v:
In Neuroscience Letters 14 September 2024 839