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Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

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樓主: 富裕
51#
發(fā)表于 2025-3-30 09:10:54 | 只看該作者
52#
發(fā)表于 2025-3-30 14:22:19 | 只看該作者
PL,P - Point-Line Minimal Problems Under Partial Visibility in Three Views,perspective cameras when each line is incident to at most one point. This is a large class of interesting minimal problems that allows missing observations in images due to occlusions and missed detections. There is an infinite number of such minimal problems; however, we show that they can be reduc
53#
發(fā)表于 2025-3-30 19:01:08 | 只看該作者
54#
發(fā)表于 2025-3-30 22:25:45 | 只看該作者
55#
發(fā)表于 2025-3-31 01:51:03 | 只看該作者
Deformable Style Transfer,ly ignoring geometry. We propose deformable style transfer (DST), an optimization-based approach that jointly stylizes the texture and geometry of a content image to better match a style image. Unlike previous geometry-aware stylization methods, our approach is neither restricted to a particular dom
56#
發(fā)表于 2025-3-31 08:40:46 | 只看該作者
57#
發(fā)表于 2025-3-31 12:52:35 | 只看該作者
58#
發(fā)表于 2025-3-31 14:01:21 | 只看該作者
RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax,by the softmax cross-entropy loss generally show excessive intra-class variations. We argue that, because the traditional softmax losses aim to optimize only the relative differences between intra-class and inter-class distances (.), it cannot obtain representative class prototypes (class weights/ce
59#
發(fā)表于 2025-3-31 21:35:25 | 只看該作者
Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction,ulation is expensive and has large domain gaps. Instead, we directly simulate the outputs of the self-driving vehicle’s perception and prediction system, enabling realistic motion planning testing. Specifically, we use paired data in the form of ground truth labels and real perception and prediction
60#
發(fā)表于 2025-3-31 23:49:03 | 只看該作者
Jeffrey A. Riffell,John G. HildebrandFully Convolutional Networks (dilatedFCN), which adopt dilated convolutions in the backbone networks to extract high-resolution feature maps for achieving high-performance segmentation performance. However, due to many convolution operations are conducted on the high-resolution feature maps, such di
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