79 citations · 276 across the 17 of their papers we have counts for
7 papers · 1 filter
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking
David M. Chan, Sudheendra Vijayanarasimhan, David A. Ross +1
Automatic video captioning aims to train models to generate text descriptions for all segments in a video, however, the most effective approaches require large amounts of manual an…
Grounding Human-to-Vehicle Advice for Self-driving Vehicles
Jinkyu Kim, Teruhisa Misu, Yi-Ting Chen +2
Recent success suggests that deep neural control networks are likely to be a key component of self-driving vehicles. These networks are trained on large datasets to imitate human a…
Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention
Ye Xia, Jinkyu Kim, John Canny +2
Inspired by human vision, we propose a new periphery-fovea multi-resolution driving model that predicts vehicle speed from dash camera videos. The peripheral vision module of the m…
Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics
Biye Jiang, David M. Chan, Tianhao Zhang +1
The internal states of most deep neural networks are difficult to interpret, which makes diagnosis and debugging during training challenging. Activation maximization methods are wi…
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data
Xinlei Pan, Sung-Li Chiang, John Canny
Self-driving vehicle vision systems must deal with an extremely broad and challenging set of scenes. They can potentially exploit an enormous amount of training data collected from…
Textual Explanations for Self-Driving Vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell +2
Deep neural perception and control networks have become key components of self-driving vehicles. User acceptance is likely to benefit from easy-to-interpret textual explanations wh…