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

祝賀

European and Transatlantic Studiese distance to class centroids, in the embedding space. The method is tested on standard benchmarks for semi-supervised learning, where it matches state of the art performance and on the problem of face expression recognition where it increases the accuracy by a noticeable margin.

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arrogant

Margin-Mix: Semi-Supervised Learning for Face Expression Recognition,de, for an input image, both an embedding descriptor and a prediction. The unlabeled data is combined with the labeled one in order to provide synthetic data, which describes better the input space. The network is asked to provide a large margin between clusters, while new data is self-labeled by th

aerobic

,Principal Feature Visualisation in?Convolutional Neural Networks,k to map principal features from the final convolutional layer to the original image space as RGB channels. By working on a batch of images we can extract contrasting features, not just the most dominant ones with respect to the classification. This allows us to differentiate between several feature

終點(diǎn)

Progressive Refinement Network for Occluded Pedestrian Detection,d pedestrians, PRNet achieves sequential refinement by three phases: Finding high-confident anchors of visible parts, calibrating such anchors to a full-body template derived from occlusion statistics, and then adjusting the calibrated anchors to final full-body regions. Unlike conventional methods

格子架

Monocular Real-Time Volumetric Performance Capture, for expensive multi-view systems or cumbersome pre-acquisition of a personalized template model. Our system reconstructs a fully textured 3D human from each frame by leveraging Pixel-Aligned Implicit Function (PIFu). While PIFu achieves high-resolution reconstruction in a memory-efficient manner, i

HARD

搖擺

,Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and?Objects for 3D Hand Pose Eion. We show that the accuracy of state-of-the-art methods can drop, and that they fail mostly on poses absent from the training set. Unfortunately, since the space of hand poses is highly dimensional, it is inherently not feasible to cover the whole space densely, despite recent efforts in collecti

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嚴(yán)厲批評(píng)

SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networkrning. The embedding onto the hypersphere requires no direct normalization and is easy to optimize. Our theoretical analysis shows that the proposed dissimilarity measure, denoted the Squared root of the Euclidean distance and the Norm distance (SEN), forces embedding points to be attracted to its c