We present a novel method for weakly-supervised action segmentation and unseen error detection in anomalous instructional videos.
Experimental results show efficacy of the proposed methods both qualitatively and quantitatively in two domains of cooking and assembly.
Third, the semantic context of the scene are modeled and take into account the environmental constraints that potentially influence the future motion.
In the proposed approach, a predictive distribution of future forecast is jointly modeled with the uncertainty of predictions.
This paper examines the problem of dynamic traffic scene classification under space-time variations in viewpoint that arise from video captured on-board a moving vehicle.
2 code implementations • 17 Nov 2018 • Iddo Drori, Isht Dwivedi, Pranav Shrestha, Jeffrey Wan, Yueqi Wang, Yunchu He, Anthony Mazza, Hugh Krogh-Freeman, Dimitri Leggas, Kendal Sandridge, Linyong Nan, Kaveri Thakoor, Chinmay Joshi, Sonam Goenka, Chen Keasar, Itsik Pe'er
In the spirit of reproducible research we make our data, models and code available, aiming to set a gold standard for purity of training and testing sets.
We propose SketchParse, the first deep-network architecture for fully automatic parsing of freehand object sketches.