40 citations · 48 across the 5 of their papers we have counts for
9 papers
Interactive Multi-Class Tiny-Object Detection
Chunggi Lee, Seonwook Park, Heon Song +5
Annotating tens or hundreds of tiny objects in a given image is laborious yet crucial for a multitude of Computer Vision tasks. Such imagery typically contains objects from various…
Learning Visual Context by Comparison
Minchul Kim, Jongchan Park, Seil Na +2
Finding diseases from an X-ray image is an important yet highly challenging task. Current methods for solving this task exploit various characteristics of the chest X-ray image, bu…
Reducing Domain Gap by Reducing Style Bias
Hyeonseob Nam, HyunJae Lee, Jongchan Park +2
Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies sug…
Visuomotor Understanding for Representation Learning of Driving Scenes
Seokju Lee, Junsik Kim, Tae-Hyun Oh +4
Dashboard cameras capture a tremendous amount of driving scene video each day. These videos are purposefully coupled with vehicle sensing data, such as from the speedometer and ine…
PseudoEdgeNet: Nuclei Segmentation only with Point Annotations
Inwan Yoo, Donggeun Yoo, Kyunghyun Paeng
Nuclei segmentation is one of the important tasks for whole slide image analysis in digital pathology. With the drastic advance of deep learning, recent deep networks have demonstr…
Learning Loss for Active Learning
Donggeun Yoo, In So Kweon
The performance of deep neural networks improves with more annotated data. The problem is that the budget for annotation is limited. One solution to this is active learning, where…