1 citations · 3 across the 4 of their papers we have counts for
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Few-Shot Object Detection via Knowledge Transfer
Geonuk Kim, Hong-Gyu Jung, Seong-Whan Lee
Conventional methods for object detection usually require substantial amounts of training data and annotated bounding boxes. If there are only a few training data and annotations,…
Counterfactual Explanation Based on Gradual Construction for Deep Networks
Hong-Gyu Jung, Sin-Han Kang, Hee-Dong Kim +2
To understand the black-box characteristics of deep networks, counterfactual explanation that deduces not only the important features of an input space but also how those features…
Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning
Jin-Woo Seo, Hong-Gyu Jung, Seong-Whan Lee
Few-shot learning aims to classify unseen classes with a few training examples. While recent works have shown that standard mini-batch training with a carefully designed training s…
Few-Shot Learning with Geometric Constraints
Hong-Gyu Jung, Seong-Whan Lee
In this article, we consider the problem of few-shot learning for classification. We assume a network trained for base categories with a large number of training examples, and we a…