Zobrazeno 1 - 10
of 576
pro vyhledávání: '"Wu, ZongZe"'
Autor:
Lin, Yuxuan, Chang, Yang, Tong, Xuan, Yu, Jiawen, Liotta, Antonio, Huang, Guofan, Song, Wei, Zeng, Deyu, Wu, Zongze, Wang, Yan, Zhang, Wenqiang
In the advancement of industrial informatization, Unsupervised Industrial Anomaly Detection (UIAD) technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. Whi
Externí odkaz:
http://arxiv.org/abs/2410.21982
Existing benchmarks like NLGraph and GraphQA evaluate LLMs on graphs by focusing mainly on pairwise relationships, overlooking the high-order correlations found in real-world data. Hypergraphs, which can model complex beyond-pairwise relationships, o
Externí odkaz:
http://arxiv.org/abs/2410.10083
We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models. We introduce an encoder based iterative inversion technique. The inversion network is conditioned on the input image and
Externí odkaz:
http://arxiv.org/abs/2408.08332
Few-shot anomaly detection methods can effectively address data collecting difficulty in industrial scenarios. Compared to 2D few-shot anomaly detection (2D-FSAD), 3D few-shot anomaly detection (3D-FSAD) is still an unexplored but essential task. In
Externí odkaz:
http://arxiv.org/abs/2406.18941
In industrial scenarios, it is crucial not only to identify anomalous items but also to classify the type of anomaly. However, research on anomaly multi-classification remains largely unexplored. This paper proposes a novel and valuable research task
Externí odkaz:
http://arxiv.org/abs/2406.05645
Autor:
Nitzan, Yotam, Wu, Zongze, Zhang, Richard, Shechtman, Eli, Cohen-Or, Daniel, Park, Taesung, Gharbi, Michaël
We introduce a novel diffusion transformer, LazyDiffusion, that generates partial image updates efficiently. Our approach targets interactive image editing applications in which, starting from a blank canvas or an image, a user specifies a sequence o
Externí odkaz:
http://arxiv.org/abs/2404.12382
Autor:
Pei, Wenjie, Xu, Weina, Wu, Zongze, Li, Weichao, Wang, Jinfan, Lu, Guangming, Wang, Xiangrong
The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the representation
Externí odkaz:
http://arxiv.org/abs/2401.00755
Head detection provides distribution information of pedestrian, which is crucial for scene statistical analysis, traffic management, and risk assessment and early warning. However, scene complexity and large-scale variation in the real world make acc
Externí odkaz:
http://arxiv.org/abs/2310.09492
Semantic segmentation is a classic and fundamental computer vision problem dedicated to assigning each pixel with its corresponding class. Some recent methods introduce edge-based information for improving the segmentation performance. However these
Externí odkaz:
http://arxiv.org/abs/2303.10307
As a fundamental computer vision task, crowd counting plays an important role in public safety. Currently, deep learning based head detection is a promising method for crowd counting. However, the highly concerned object detection networks cannot be
Externí odkaz:
http://arxiv.org/abs/2212.11542