Researchers from NVIDIA just received the #CVPR2018 “Best Paper Honorable Mention Award,” for their paper, “SPLATNet: Sparse Lattice Networks for Point Cloud Processing."

paper : https://arxiv.org/abs/1802.08275

https://news.developer.nvidia.com/nvidia-splatnet-research-paper-wins-a-major-cvpr-2018-award/

https://t.me/ArtificialIntelligenceArticles

Slides for most talks at Good Citizen at CVPR workshop are up https://goo.gl/qXzezt or http://goo.gl/8Q98vR Lots of useful advice and experience for writing and reviewing papers, how to do good research and evaluation, talks, how to organise your time #CVPR2018

https://t.me/ArtificialIntelligenceArticles

Neural Kinematic Networks for Unsupervised Motion Retargetting #CVPR2018
paper: https://arxiv.org/abs/1804.05653 Code : http://goo.gl/4TdpoA

Best paper award at #CVPR2018 :

"Taskonomy: Disentangling Task Transfer Learning"

Abstract : Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks. Knowing this structure has notable values; it is the concept underlying transfer learning and provides a principled way for identifying redundancies across tasks, e.g., to seamlessly reuse supervision among related tasks or solve many tasks in one system without piling up the complexity. We proposes a fully computational approach for modeling the structure of space of visual tasks (...).

Paper: https://arxiv.org/pdf/1804.08328.pdf
Data: http://taskonomy.stanford.edu

#award #artificialintelligence #deeplearning #transferlearning

Best paper award at #CVPR2018 main idea: study twenty five different visual tasks to understand how & when transfer learning works from one task to another, reducing demand for labelled data.

Paper: arxiv.org/pdf/1804.08328
Data: taskonomy.stanford.edu https://t.me/ArtificialIntelligenceArticles