SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning

1 code implementation • CVPR 2021 • Colorado J Reed, Sean Metzger, Aravind Srinivas, Trevor Darrell, Kurt Keutzer

A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations.

Data Augmentation Representation Learning +1

Region Similarity Representation Learning

1 code implementation • ICCV 2021 • Tete Xiao, Colorado J Reed, Xiaolong Wang, Kurt Keutzer, Trevor Darrell

We present Region Similarity Representation Learning (ReSim), a new approach to self-supervised representation learning for localization-based tasks such as object detection and segmentation.

Instance Segmentation Object +5

DETReg: Unsupervised Pretraining with Region Priors for Object Detection

1 code implementation • CVPR 2022 • Amir Bar, Xin Wang, Vadim Kantorov, Colorado J Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson

Recent self-supervised pretraining methods for object detection largely focus on pretraining the backbone of the object detector, neglecting key parts of detection architecture.

Few-Shot Learning Few-Shot Object Detection +6

SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data

no code implementations • 17 Jan 2022 • Dhileeban Kumaresan, Richard Wang, Ernesto Martinez, Richard Cziva, Alberto Todeschini, Colorado J Reed, Hossein Vahabi

Accurate short-term PV power prediction enables operators to maximize the amount of power obtained from PV panels and safely reduce the reserve energy needed from fossil fuel sources.

Snowpack Estimation in Key Mountainous Water Basins from Openly-Available, Multimodal Data Sources

1 code implementation • 8 Aug 2022 • Malachy Moran, Kayla Woputz, Derrick Hee, Manuela Girotto, Paolo D'Odorico, Ritwik Gupta, Daniel Feldman, Puya Vahabi, Alberto Todeschini, Colorado J Reed

Accurately estimating the snowpack in key mountainous basins is critical for water resource managers to make decisions that impact local and global economies, wildlife, and public policy.

Refine and Represent: Region-to-Object Representation Learning

1 code implementation • 25 Aug 2022 • Akash Gokul, Konstantinos Kallidromitis, Shufan Li, Yusuke Kato, Kazuki Kozuka, Trevor Darrell, Colorado J Reed

Recent works in self-supervised learning have demonstrated strong performance on scene-level dense prediction tasks by pretraining with object-centric or region-based correspondence objectives.

Object Representation Learning +4