no code implementations • 7 Oct 2019 • Steve Branson, Jan Dirk Wegner, David Hall, Nico Lang, Konrad Schindler, Pietro Perona
We believe this is the first work to exploit publicly available image data for fine-grained tree mapping at city-scale, respectively over many thousands of trees.
no code implementations • ICCV 2019 • Oren Rippel, Sanjay Nair, Carissa Lew, Steve Branson, Alexander G. Anderson, Lubomir Bourdev
We present a new algorithm for video coding, learned end-to-end for the low-latency mode.
no code implementations • CVPR 2018 • Grant Van Horn, Steve Branson, Scott Loarie, Serge Belongie, Pietro Perona
We introduce a method for efficiently crowdsourcing multiclass annotations in challenging, real world image datasets.
no code implementations • CVPR 2017 • Steve Branson, Grant van Horn, Pietro Perona
We develop specialized models and algorithms for binary annotation, part keypoint annotation, and sets of bounding box annotations.
no code implementations • CVPR 2015 • Grant Van Horn, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry, Panos Ipeirotis, Pietro Perona, Serge Belongie
We worked with bird experts to measure the quality of popular datasets like CUB-200-2011 and ImageNet and found class label error rates of at least 4%.
no code implementations • 11 Jun 2014 • Steve Branson, Grant van Horn, Serge Belongie, Pietro Perona
We perform a detailed investigation of state-of-the-art deep convolutional feature implementations and fine-tuning feature learning for fine-grained classification.
no code implementations • CVPR 2014 • Catherine Wah, Grant van Horn, Steve Branson, Subhransu Maji, Pietro Perona, Serge Belongie
Current human-in-the-loop fine-grained visual categorization systems depend on a predefined vocabulary of attributes and parts, usually determined by experts.
Fine-Grained Visual Categorization General Classification +2
no code implementations • CVPR 2014 • Steve Branson, Kristjan Eldjarn Hjorleifsson, Pietro Perona
Human annotators may be thought of as helping translate the old annotations into the new ones.
no code implementations • CVPR 2013 • Steve Branson, Oscar Beijbom, Serge Belongie
Our method is shown to be 10-50 times faster than SVMstruct for cost-sensitive multiclass classification while being about as fast as the fastest 1-vs-all methods for multiclass classification.