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.

Representation Learning Self-Supervised Learning +2

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.

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.

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 +4

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-detection +4

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