Now showing items 11-14 of 14
Interactively Guiding Semi-Supervised Clustering via Attribute-based Explanations
(Virginia Tech, 2015-07-01)
Unsupervised image clustering is a challenging and often ill-posed problem. Existing image descriptors fail to capture the clustering criterion well, and more importantly, the criterion itself may depend on (unknown) user ...
The Art of Deep Connection - Towards Natural and Pragmatic Conversational Agent Interactions
(Virginia Tech, 2017-07-12)
As research in Artificial Intelligence (AI) advances, it is crucial to focus on having seamless communication between humans and machines in order to effectively accomplish tasks. Smooth human-machine communication requires ...
Classification of Faults in Railway Ties Using Computer Vision and Machine Learning
(Virginia Tech, 2017-06-30)
This work focuses on automated classification of railway ties based on their condition using aerial imagery. Four approaches are explored and compared to achieve this goal - handcrafted features, HOG features, transfer ...
Handling Invalid Pixels in Convolutional Neural Networks
(Virginia Tech, 2020-05-29)
Most neural networks use a normal convolutional layer that assumes that all input pixels are valid pixels. However, pixels added to the input through padding result in adding extra information that was not initially present. ...