为此,我们提出了一种基于核感知图提示学习(Kernel-Aware Graph Prompt Learning)的框架,简称 KAG-prompt,用于在 FSAD 任务中推理视觉特征的跨层关系。具体而言,我们构建了一个核感知层次图(Kernel-aware Hierarchical Graph),其中不同层次的特征(关注不同尺度的异常区域)被视为图的节点,而任意两个节点之间的关系则构...
The at least one processor is configured to generate a low-resolution image from the burst of image frames. The at least one processor is also configured to estimate a blur kernel based on the burst of image frames. The at least one processor is further configured to perform deconvolution on...
RTX Configuration in µVision µVision also provides kernel aware debug for RTX which makes it easy to view kernel status and details together with information about the event timeline and resource loading RTX Kernel Aware Debug and Event Viewer in µVision...
Blind Super-Resolution with Kernel-Aware Feature Refinement Numerous learning-based super-resolution (SR) methods that assume the blur kernel is known in advance or hand-crafted perform excellently on the synthesize... Z Wu,Y Lu,G Li,... - Chinese Conference on Pattern Recognition & Computer ...
Kernel-aware warp scheduling is employed to avoid the starvation problem because that greedy-then-oldest (GTO) warp scheduling would result in that some kernels have lower priority to issue instructions [7], but it does not consider that L1D would be blocked by memory-intensive kernel and ...
Time-aware Large Kernel Generation 对于上面的一维卷积,确定计算每个时间步长的左右偏移量是很重要的。该方法的关键是用一个具有自适应时间感知的核卷积运算,其核大小作为单个时间步长的学习函数,可随时间的变化而变化。也就是说,我们将提出如何针对每个时间步去学习求和核的左右偏移量。 具体地,我们提出使用一个函数...
MLKP CVPR18 Paper: Multi-scale Location-aware Kernel Representation for Object Detection. Paper can be found inarXivandCVPR2018. MLKP is a novel compact, location-aware kernel approximation method to represent object proposals for effective object detection. Our method is among the first which explo...
MICCAI 2022 : Lesion-aware Dynamic Kernel for Polyp Segmentation (Pytorch implementation). - ReaFly/LDNet
Kernel Aware Resampler CVPR 2023·Michael Bernasconi,Abdelaziz Djelouah,Farnood Salehi,Markus Gross,Christopher Schroers· Deep learning based methods for super-resolution have become state-of-the-art and outperform traditional approaches by a significant margin. From the initial models designed for ...
In this work, we make full use of context information (namely geometrical structure of images) in order to learn better context-aware similarities (a.k.a. kernels) between images. We reformulate context-aware kernel design as a feed-forward network that outputs explicit kernel mapping features....