1 citations · 2 across the 4 of their papers we have counts for
4 papers
Collecting Consistently High Quality Object Tracks with Minimal Human Involvement by Using Self-Supervised Learning to Detect Tracker Errors
Samreen Anjum, Suyog Jain, Danna Gurari
We propose a hybrid framework for consistently producing high-quality object tracks by combining an automated object tracker with little human input. The key idea is to tailor a mo…
VQA Therapy: Exploring Answer Differences by Visually Grounding Answers
Chongyan Chen, Samreen Anjum, Danna Gurari
Visual question answering is a task of predicting the answer to a question about an image. Given that different people can provide different answers to a visual question, we aim to…
Grounding Answers for Visual Questions Asked by Visually Impaired People
Chongyan Chen, Samreen Anjum, Danna Gurari
Visual question answering is the task of answering questions about images. We introduce the VizWiz-VQA-Grounding dataset, the first dataset that visually grounds answers to visual…
CrowdMOT: Crowdsourcing Strategies for Tracking Multiple Objects in Videos
Samreen Anjum, Chi Lin, Danna Gurari
Crowdsourcing is a valuable approach for tracking objects in videos in a more scalable manner than possible with domain experts. However, existing frameworks do not produce high qu…