preface

Recently open source projects are really many, on the one hand, CVPR 2020 acceptance results released, so a large number of CVPR 2020 papers and the corresponding code is gradually released. This article will focus on three noteworthy open source projects (PointRend, EfficientDet and SimCLR) in recent days, and will introduce open source projects separately for CVPR 2020 segmentation.

PointRend\

Links to papers: arxiv.org/abs/1912.08…

Code link:

Github.com/facebookres…

In fact, this paper is not only open source, but also available in CVPR 2020 (Oral). In addition, He Kaiming won 3 CVPR 2020 this year, and all of them are Oral.

The central idea of this study is to treat image segmentation as a rendering problem and to “render” high quality label graphs efficiently using classical ideas in computer graphics. Based on this idea, a new neural network module named PointRend is proposed, which adaptively selects a group of non-uniform points to calculate labels using subdivision strategy.

Pointrends can be incorporated into popular meta-architectures for instance segmentation (e.g. Mask R-CNN) and semantic segmentation (e.g. FCN). Its subdivision strategy uses one order of magnitude fewer floating point operations than direct intensive computations, allowing efficient computation of high-resolution split graphs.

Apply PointRend to instance split \

Compare PointRend with the default 4×conv head in Mask R-CNN in Table 1.

For the interpretation of the paper, see:

PointRend: Image segmentation as a rendering problem, significantly improving semantic/instance segmentation performance!

EfficientDet\

Links to papers: arxiv.org/abs/1911.09…

Code link:

Github.com/google/auto…

In fact, this paper is also included in CVPR 2020!

When the paper just came out, I saw 51.0 mAP on COCO dataset! Stunned! The model is lightweight and efficient, and the code was open source 10 days ago, so everyone can use it.

Efficientdet-d6 efficientdet-d7 is efficientdet-d6 and efficientdet-d0 is efficientdet-d6. Efficientdet-d6 efficientDet-D6 efficientDet-D6 efficientDet-D6 EfficientDet-D6 EfficientDet-D6 EfficientDet-D6 EfficientDet-D6

For the interpretation of the paper, see:

EfficientNet and EfficientDet

SimCLR\

Links to papers: arxiv.org/abs/2002.05…

Code link:

Github.com/google-rese…

Two recent great books on unsupervised learning:

Hinton group: SimCLR

He Kaiming et al. : MoCo V2

Note: MoCo has been included in CVPR 2020 (Oral), and MoCo V2 is an improved version of MoCo, whose performance has surpassed that of SimCLR.

Introduction of SimCLR

How to evaluate the Hinton Group’s new work, SimCLR?

www.zhihu.com/question/37…

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