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

季雨

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外貌

Weakly-Supervised Learning of Human Dynamics, minimized, i.e.?no ground truth forces and moments are required during training. The proposed method achieves state-of-the-art results in terms of ground reaction force, ground reaction moment and joint torque regression and is able to maintain good performance on substantially reduced sets.

alliance

Embedding Propagation: Smoother Manifold for Few-Shot Classification, propagation to a transductive classifier achieves new state-of-the-art results in .Imagenet, .Imagenet, Imagenet-FS, and CUB. Furthermore, we show that embedding propagation consistently improves the accuracy of the models in multiple semi-supervised learning scenarios by up?to 16% points. The prop

IVORY

Category Level Object Pose Estimation via Neural Analysis-by-Synthesis,zation of the object pose, shape and appearance in a joint manner and we experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone. When provided with depth measurements, to overcome scale ambiguities, the method can accurately recover the full 6D

保守黨

hankering

PL,P - Point-Line Minimal Problems Under Partial Visibility in Three Views,g and 3D reconstruction, we present several natural subfamilies of camera-minimal problems as well as compute solution counts for all camera-minimal problems which have less than 300 solutions for generic data.

不持續(xù)就爆

Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification,ach scale factor is optimal, which are used as guidance to enhance the content-aware scale factor prediction. Consequently, our model can more accurately predict and recover the content-aware details, and achieve state-of-the-art performances on four LR re-id datasets.

MEEK

Neural Wireframe Renderer: Learning Wireframe to Image Translations,sentation learned from both images and wireframes. In our model, structural constraints are explicitly enforced by learning a joint representation in a shared encoder network that must support the generation of both images and wireframes. Experiments on a wireframe-scene dataset show that our wirefr

Champion