20 citations · 45 across the 13 of their papers we have counts for
6 papers · 1 filter
SID-NISM: A Self-supervised Low-light Image Enhancement Framework
Lijun Zhang, Xiao Liu, Erik Learned-Miller +1
When capturing images in low-light conditions, the images often suffer from low visibility, which not only degrades the visual aesthetics of images, but also significantly degenera…
Shot in the Dark: Few-Shot Learning with No Base-Class Labels
Zitian Chen, Subhransu Maji, Erik Learned-Miller
Few-shot learning aims to build classifiers for new classes from a small number of labeled examples and is commonly facilitated by access to examples from a distinct set of 'base c…
Improving Face Recognition by Clustering Unlabeled Faces in the Wild
Aruni RoyChowdhury, Xiang Yu, Kihyuk Sohn +2
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reduci…
Cross-Supervised Object Detection
Zitian Chen, Zhiqiang Shen, Jiahui Yu +1
After learning a new object category from image-level annotations (with no object bounding boxes), humans are remarkably good at precisely localizing those objects. However, buildi…
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma +5
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years. Both of these problems, however, suffer fr…
In Defense of Grid Features for Visual Question Answering
Huaizu Jiang, Ishan Misra, Marcus Rohrbach +2
Popularized as 'bottom-up' attention, bounding box (or region) based visual features have recently surpassed vanilla grid-based convolutional features as the de facto standard for…