23 citations · 24 across the 3 of their papers we have counts for
4 papers
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition
Ping Hu, Ximeng Sun, Kate Saenko +1
Learning from a few examples is a challenging task for machine learning. While recent progress has been made for this problem, most of the existing methods ignore the compositional…
Automating Image Analysis by Annotating Landmarks with Deep Neural Networks
Mikhail Breslav, Tyson L. Hedrick, Stan Sclaroff +1
Image and video analysis is often a crucial step in the study of animal behavior and kinematics. Often these analyses require that the position of one or more animal landmarks are…
Discovering Useful Parts for Pose Estimation in Sparsely Annotated Datasets
Mikhail Breslav, Tyson L. Hedrick, Stan Sclaroff +1
Our work introduces a novel way to increase pose estimation accuracy by discovering parts from unannotated regions of training images. Discovered parts are used to generate more ac…
A Bayesian Approach for Online Classifier Ensemble
Qinxun Bai, Henry Lam, Stan Sclaroff
We propose a Bayesian approach for recursively estimating the classifier weights in online learning of a classifier ensemble. In contrast with past methods, such as stochastic grad…