Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

BLAND

,Addressing Heterogeneity in?Federated Learning via?Distributional Transformation,s shows that . outperforms state-of-the-art FL methods and data augmentation methods under various settings and different degrees of client distributional heterogeneity (e.g., for CelebA and 100% heterogeneity . has accuracy of 80.4% vs. 72.1% or lower for other SOTA approaches).

Tonometry

OPINE

,Colorization for?, Marine Plankton Images,ments and comparisons with state-of-the-art approaches are presented to show that our method achieves a substantial improvement over previous methods on color restoration of scientific plankton image data.

知識分子

Between

,A Cloud 3D Dataset and?Application-Specific Learned Image Compression in?Cloud 3D,hich makes it feasible to reduce the model complexity to accelerate compression computation. We evaluated our models on six gaming image datasets. The results show that our approach has similar rate-distortion performance as a state-of-the-art learned image compression algorithm, while obtaining abo

健忘癥

,AutoTransition: Learning to?Recommend Video Transition Effects,k. Then we propose a model to learn the matching correspondence from vision/audio inputs to video transitions. Specifically, the proposed model employs a multi-modal transformer to fuse vision and audio information, as well as capture the context cues in sequential transition outputs. Through both q

組成

Aesthete

inconceivable

共同生活

Stephan Neuhaus,Bernhard Plattnerctive for the probe’s future performance, ameliorating the sales forecasts of all state-of-the-art models on the recent VISUELLE fast-fashion dataset. We also show that POP reflects the ground-truth popularity of new styles (ensembles of clothing items) on the Fashion Forward benchmark, demonstratin