28 citations · 79 across the 28 of their papers we have counts for
6 papers · 1 filter
Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes
Chenhongyi Yang, Vitaly Ablavsky, Kaihong Wang +2
While visual object detection with deep learning has received much attention in the past decade, cases when heavy intra-class occlusions occur have not been studied thoroughly. In…
A method for detecting text of arbitrary shapes in natural scenes that improves text spotting
Qitong Wang, Yi Zheng, Margrit Betke
Understanding the meaning of text in images of natural scenes like highway signs or store front emblems is particularly challenging if the text is foreshortened in the image or the…
Scraping Social Media Photos Posted in Kenya and Elsewhere to Detect and Analyze Food Types
Kaihong Wang, Mona Jalal, Sankara Jefferson +3
Monitoring population-level changes in diet could be useful for education and for implementing interventions to improve health. Research has shown that data from social media sourc…
Deep Neural Network for Semantic-based Text Recognition in Images
Yi Zheng, Qitong Wang, Margrit Betke
State-of-the-art text spotting systems typically aim to detect isolated words or word-by-word text in images of natural scenes and ignore the semantic coherence within a region of…
Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch
Danna Gurari, Yinan Zhao, Suyog Dutt Jain +2
Foreground object segmentation is a critical step for many image analysis tasks. While automated methods can produce high-quality results, their failures disappoint users in need o…
BUOCA: Budget-Optimized Crowd Worker Allocation
Mehrnoosh Sameki, Sha Lai, Kate K. Mays +3
Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We here show that the resultin…