Meta-Learning with Differentiable Convex Optimization #CVPR2019 Oral

Few-shot learning SoTA on miniImageNet, tieredImageNet, CIFAR-FS, and FC100

Github
https://github.com/kjunelee/MetaOptNet

ArXiv

https://arxiv.org/abs/1904.03758

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection #CVPR2019


Key component to close the gap between image & LiDAR based 3D object detection may be simply the representation of 3D information

SOTA on KITTI

https://arxiv.org/abs/1812.07179v4

Speech2Face: Learning the Face Behind a Voice #CVPR2019



ArXiv
arxiv.org/abs/1905.09773
Project
speech2face.github.io

All 1,294 papers at #CVPR2019
Index: http://openaccess.thecvf.com/content_CVPR_2019/html/
#ArtificialIntelligence #DeepLearning #MachineLearning

A key conference quality indicator is low paper acceptance rates. The CVPR 2019 paper acceptance rate dropped to 25.1 percent from last year’s 29.6 percent 🤓☝️

The list of all 1300 research papers accepted for CVPR 2019 is available here: http://openaccess.thecvf.com/CVPR2019.py

Given you spend 1 hour to read 1 article and the rate of 8 articles per day, it will take you about 6 months to read all of them. You'd better start right now 🙃

#CVPR2019 #computervision #patternrecognition #deeplearning #machinelearning

#CVPR2019 Videos :Videos from #CVPR2019 talks are gradually uploaded on YouTube .if you missed #CVPR2019 here is the treasure trove of videos thanks to the ComputerVisionFoundation


https://www.youtube.com/channel/UC0n76gicaarsN_Y9YShWwhw/videos

https://t.me/ArtificialIntelligenceArticles

They say a picture is worth a thousand words. Sure, but the real trick is realizing a bot that draws pictures is using only a dozen. Throw in an ability to visualize an entire story and one day this bot could be working in the movies. #CVPR2019

https://www.microsoft.com/en-us/research/blog/a-picture-from-a-dozen-words-a-drawing-bot-for-realizing-everyday-scenes-and-even-stories/?OCID=msr_blog_drawbot_cvpr_tw

STEAL, a new algorithm developed by NVIDIA Research and being presented this week at #CVPR2019, automatically refines the boundaries of objects in training datasets, making them more exact.

Link to the blog: https://news.developer.nvidia.com/nvidia-research-released-at-cvpr-helps-developers-create-better-visual-datasets/

One of our best of #cvpr2019 is @NvidiaAI STEAL - a new semantic boundary detector for precisely detecting & predicting where an object begins & ends -outperforming past works. A really intelligent way to refine segmentation datasets and train better segmentation models

Read at https://arxiv.org/pdf/1904.07934.pdf
code(@PyTorch):github.com/nv-tlabs/STEAL