by Martina Megaro | Jun 27, 2016 | Machine Learning
Learning Activity Progression in LSTMs for Activity Detection and Early Detection In this work we improve training of temporal deep models to better learn activity progression for activity detection and early detection tasks. June 27, 2016IEEE Conference on Computer...
by Sarah Frigg | Jun 24, 2016 | Machine Learning, Visual Computing
Learning Activity Progression in LSTMs for Activity Detection and Early Detection In this work we improve training of temporal deep models to better learn activity progression for activity detection and early detection. June 24, 2016IEEE Conference on Computer...
by Sarah Frigg | Jun 24, 2016 | Visual Computing
Harnessing Object and Scene Semantics for Large-Scale Video Understanding We propose a novel object- and scene-based semantic fusion network and representation. June 24, 2016Accepted at IEEE Conference on Computer Vision Pattern Recognition (CVPR) 2016 ...
by Sarah Frigg | Jun 24, 2016 | Machine Learning, Visual Computing
Completed Semi-supervised Vocabulary-informed Learning We propose the notion of semi-supervised vocabulary-informed learning to alleviate the above mentions challenges and address problems of supervised, zero-shot and open set recognition using a unified framework....
by Sarah Frigg | Jun 24, 2016 | Machine Learning, Visual Computing
Learning Online Smooth Predictors for Realtime Camera Planning using Recurrent Decision Trees We propose a recurrent decision tree framework that can directly incorporate temporal consistency into a data-driven predictor, as well as a learning algorithm that can...
