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

Taylor

书目名称Computer Vision – ECCV 2020影响因子(影响力)




书目名称Computer Vision – ECCV 2020影响因子(影响力)学科排名




书目名称Computer Vision – ECCV 2020网络公开度




书目名称Computer Vision – ECCV 2020网络公开度学科排名




书目名称Computer Vision – ECCV 2020被引频次




书目名称Computer Vision – ECCV 2020被引频次学科排名




书目名称Computer Vision – ECCV 2020年度引用




书目名称Computer Vision – ECCV 2020年度引用学科排名




书目名称Computer Vision – ECCV 2020读者反馈




书目名称Computer Vision – ECCV 2020读者反馈学科排名

Indolent

字形刻痕

Dual Adversarial Network: Toward Real-World Noise Removal and Noise Generation,ates the research of noise generation, aiming at synthesizing more clean-noisy image pairs to facilitate the training of deep denoisers. In this work, we propose a novel unified framework to simultaneously deal with the noise removal and noise generation tasks. Instead of only inferring the posterio

反对

Linguistic Structure Guided Context Modeling for Referring Image Segmentation,ence is crucial to distinguish the referent from the background. Existing methods either insufficiently or redundantly model the multimodal context. To tackle this problem, we propose a “gather-propagate-distribute” scheme to model multimodal context by cross-modal interaction and implement this sch

incision

Self-Help-Group

Robust Re-Identification by Multiple Views Knowledge Distillation,a large drop in performance for single image queries (e.g., Image-To-Video setting). Recent works address this severe degradation by transferring . from a Video-based network to an Image-based one. In this work, we devise a training strategy that allows the transfer of a superior knowledge, arising

Self-Help-Group

拥挤前

,RhyRNN: Rhythmic RNN for Recognizing Events in Long and Complex Videos,ains a challenge. One particular reason is that events in long and complex videos can consist of multiple heterogeneous sub-activities (in terms of rhythms, activity variants, composition order, etc.) within quite a long period. This fact brings about two main difficulties: excessive/varying length

SPALL

过份好问

Weighing Counts: Sequential Crowd Counting by Reinforcement Learning,o existing counting models that directly output count values, we divide one-step estimation into a sequence of much easier and more tractable sub-decision problems. Such sequential decision nature corresponds exactly to a physical process in reality—scale weighing. Inspired by scale weighing, we pro