Now showing items 1-6 of 6
CloudCV: Deep Learning and Computer Vision on the Cloud
(Virginia Tech, 2016-06-20)
We are witnessing a proliferation of massive visual data. Visual content is arguably the fastest growing data on the web. Photo-sharing websites like Flickr and Facebook now host more than 6 and 90 billion photos, respectively. ...
Role of Premises in Visual Question Answering
(Virginia Tech, 2017-06-12)
In this work, we make a simple but important observation questions about images often contain premises -- objects and relationships implied by the question -- and that reasoning about premises can help Visual Question ...
Natural Language Driven Image Edits using a Semantic Image Manipulation Language
(Virginia Tech, 2018-06-04)
Language provides us with a powerful tool to articulate and express ourselves! Understanding and harnessing the expressions of natural language can open the doors to a vast array of creative applications. In this work we ...
VIP: Finding Important People in Images
(Virginia Tech, 2015-06-25)
People preserve memories of events such as birthdays, weddings, or vacations by capturing photos, often depicting groups of people. Invariably, some individuals in the image are more important than others given the context ...
Low-shot Visual Recognition
(Virginia Tech, 2016-10-24)
Many real world datasets are characterized by having a long tailed distribution, with several samples for some classes and only a few samples for other classes. While many Deep Learning based solutions exist for object ...
Vision and Radar Fusion for Identification of Vehicles in Traffic
(Virginia Tech, 2015-07-30)
This report presents a method for estimating the presence and duration of preceding and lead vehicle in front of a motorcycle using an object detection algorithm guided by radar data. The video and radar data were collected ...