activity
20192025
most citedUnpriortized Autoencoder For Image Generation

4 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2025

Targeted Data Protection for Diffusion Model by Matching Training Trajectory

Hojun Lee, Mijin Koo, Yeji Song +1

Recent advancements in diffusion models have made fine-tuning text-to-image models for personalization increasingly accessible, but have also raised significant concerns regarding…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2022

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…

cs.CV2021

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…

cs.CV2019

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…

cs.LG2019★ 4 cited

Unpriortized Autoencoder For Image Generation

Jaeyoung Yoo, Hojun Lee, Nojun Kwak

In this paper, we treat the image generation task using an autoencoder, a representative latent model. Unlike many studies regularizing the latent variable's distribution by assumi…