4 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
Coreset Selection for Object Detection
Hojun Lee, Suyoung Kim, Junhoo Lee +2
Coreset selection is a method for selecting a small, representative subset of an entire dataset. It has been primarily researched in image classification, assuming there is only on…
ARC-NeRF: Area Ray Casting for Broader Unseen View Coverage in Few-shot Object Rendering
Seunghyeon Seo, Yeonjin Chang, Jayeon Yoo +3
Recent advancements in the Neural Radiance Field (NeRF) have enhanced its capabilities for novel view synthesis, yet its reliance on dense multi-view training images poses a practi…
End-to-End Multi-Object Detection with a Regularized Mixture Model
Jaeyoung Yoo, Hojun Lee, Seunghyeon Seo +2
Recent end-to-end multi-object detectors simplify the inference pipeline by removing hand-crafted processes such as non-maximum suppression (NMS). However, during training, they st…
Few-Shot Object Detection by Attending to Per-Sample-Prototype
Hojun Lee, Myunggi Lee, Nojun Kwak
Few-shot object detection aims to detect instances of specific categories in a query image with only a handful of support samples. Although this takes less effort than obtaining en…
Training Multi-Object Detector by Estimating Bounding Box Distribution for Input Image
Jaeyoung Yoo, Hojun Lee, Inseop Chung +2
In multi-object detection using neural networks, the fundamental problem is, "How should the network learn a variable number of bounding boxes in different input images?". Previous…