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

鐵塔等

,Completely Self-supervised Crowd Counting via?Distribution Matching,ould learn good representations, they require some labeled data to map these features to the end task of density estimation. We mitigate this issue with the proposed paradigm of complete self-supervision, which does not need even a single labeled image. The only input required to train, apart from a

Rejuvenate

偽造者

surmount

,Learn2Augment: Learning to?Composite Videos for?Data Augmentation in?Action Recognition,pace of possible augmented data points either at random, without knowing which augmented points will be better, or through heuristics. We propose to learn what makes a “good” video for action recognition and select only high-quality samples for augmentation. In particular, we choose video compositin

overture

占線

ingestion

,Improving Self-supervised Lightweight Model Learning via?Hard-Aware Metric Distillation,ghtweight models, which is important for many mobile devices. To address this problem, we propose a method to improve the lightweight network (as student) via distilling the metric knowledge in a larger SSL model (as teacher). We exploit the relation between teacher and student to mine the positive

Obligatory

Bother

Ventilator

0302-9743 ruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-19820-5978-3-031-19821-2Series ISSN 0302-9743 Series E-ISSN 1611-3349