Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Besnier, Victor"'
Conditional diffusion models are powerful generative models that can leverage various types of conditional information, such as class labels, segmentation masks, or text captions. However, in many real-world scenarios, conditional information may be
Externí odkaz:
http://arxiv.org/abs/2405.20324
Automating visual inspection in industrial production lines is essential for increasing product quality across various industries. Anomaly detection (AD) methods serve as robust tools for this purpose. However, existing public datasets primarily cons
Externí odkaz:
http://arxiv.org/abs/2405.04953
Autor:
Besnier, Victor, Chen, Mickael
In this technical report, we present a reproduction of MaskGIT: Masked Generative Image Transformer, using PyTorch. The approach involves leveraging a masked bidirectional transformer architecture, enabling image generation with only few steps (8~16
Externí odkaz:
http://arxiv.org/abs/2310.14400
Recent works on predictive uncertainty estimation have shown promising results on Out-Of-Distribution (OOD) detection for semantic segmentation. However, these methods struggle to precisely locate the point of interest in the image, i.e, the anomaly.
Externí odkaz:
http://arxiv.org/abs/2207.08782
In this paper, we tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. By analyzing the literature, we found that current methods are either accurate or fast but not both which limits their usability in real world appli
Externí odkaz:
http://arxiv.org/abs/2108.01634
In this paper, we show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving, by triggering a fallback behavior if a target accuracy cannot be guaranteed. We introduce a new uncertainty measure
Externí odkaz:
http://arxiv.org/abs/2105.13688
Current generative networks are increasingly proficient in generating high-resolution realistic images. These generative networks, especially the conditional ones, can potentially become a great tool for providing new image datasets. This naturally b
Externí odkaz:
http://arxiv.org/abs/1911.02888