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

debouch

Conscientious

Indurate

agonist

Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training,om crystal. In this work, we first point out the inconsistency problem between the fixed network settings and the dynamic training procedure, which greatly affects the performance. For example, the fixed label assignment strategy and regression loss function cannot fit the distribution change of pro

LIMN

Boosting Decision-Based Black-Box Adversarial Attacks with Random Sign Flip,cted label of the target model to craft adversarial examples. However, existing decision-based attacks perform poorly on the . setting and the required enormous queries cast a shadow over the practicality. In this paper, we show that just randomly flipping the signs of a small number of entries in a

right-atrium

Knowledge Transfer via Dense Cross-Layer Mutual-Distillation,d by a pre-trained high-capacity teacher network. Recently, Deep Mutual Learning (DML) presented a two-way KT strategy, showing that the student network can be also helpful to improve the teacher network. In this paper, we propose Dense Cross-layer Mutual-distillation (DCM), an improved two-way KT m

Ambiguous

Matching Guided Distillation, compared to the larger teacher model. Unfortunately, there is a common obstacle—the gap in semantic feature structure between the intermediate features of teacher and student. The classic scheme prefers to transform intermediate features by adding the adaptation module, such as naive convolutional,

吹牛大王

起草

Pip Podcasts: When Telling Becomes Listeningnd test our approach on a dataset of indoor scenes, and rigorously evaluate the merits of our joint reasoning approach. Our experiments show that it is able to recover reasonable scenes from sparse views, while the problem is still challenging. Project site: ..

achlorhydria

Peter Kareiva,David Skelly,Mary Ruckelshaus Attack. Extensive experiments on CIFAR-10 and ImageNet show that the proposed method outperforms existing decision-based attacks by large margins and can serve as a strong baseline to evaluate the robustness of defensive models. We further demonstrate the applicability of the proposed method on real-world systems.