Zobrazeno 1 - 10
of 448
pro vyhledávání: '"Wu, ZongZe"'
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
The continuous development of the photovoltaic (PV) industry has raised high requirements for the quality of monocrystalline of PV module cells. When learning to segment defect regions in PV module cell images, Tiny Hidden Cracks (THC) lead to extrem
Externí odkaz:
http://arxiv.org/abs/2211.05295
Autor:
Tian, Xudong, Zhang, Zhizhong, Wang, Cong, Zhang, Wensheng, Qu, Yanyun, Ma, Lizhuang, Wu, Zongze, Xie, Yuan, Tao, Dacheng
Information Bottleneck (IB) based multi-view learning provides an information theoretic principle for seeking shared information contained in heterogeneous data descriptions. However, its great success is generally attributed to estimate the multivar
Externí odkaz:
http://arxiv.org/abs/2206.09548