activity
20152025
most citedDepth Adaptive Deep Neural Network for Semantic Segmentation

74 citations · 132 across the 17 of their papers we have counts for

collaborators

17 papers

cs.CV2025

Unsupervised Contrastive Learning Using Out-Of-Distribution Data for Long-Tailed Dataset

Cuong Manh Hoang, Yeejin Lee, Byeongkeun Kang

This work addresses the task of self-supervised learning (SSL) on a long-tailed dataset that aims to learn balanced and well-separated representations for downstream tasks such as…

cs.CV2025

Generalized Class Discovery in Instance Segmentation

Cuong Manh Hoang, Yeejin Lee, Byeongkeun Kang

This work addresses the task of generalized class discovery (GCD) in instance segmentation. The goal is to discover novel classes and obtain a model capable of segmenting instances…

cs.CV2024★ 3 cited

Content-Aware Preserving Image Generation

Giang H. Le, Anh Q. Nguyen, Byeongkeun Kang +1

Remarkable progress has been achieved in image generation with the introduction of generative models. However, precisely controlling the content in generated images remains a chall…

cs.CV2024★ 6 cited

MSTA3D: Multi-scale Twin-attention for 3D Instance Segmentation

Duc Dang Trung Tran, Byeongkeun Kang, Yeejin Lee

Recently, transformer-based techniques incorporating superpoints have become prevalent in 3D instance segmentation. However, they often encounter an over-segmentation problem, espe…

cs.CV2024

Improving Weakly-Supervised Object Localization Using Adversarial Erasing and Pseudo Label

Byeongkeun Kang, Sinhae Cha, Yeejin Lee

Weakly-supervised learning approaches have gained significant attention due to their ability to reduce the effort required for human annotations in training neural networks. This p…

cs.CV2024

Enhancing Long-Term Person Re-Identification Using Global, Local Body Part, and Head Streams

Duy Tran Thanh, Yeejin Lee, Byeongkeun Kang

This work addresses the task of long-term person re-identification. Typically, person re-identification assumes that people do not change their clothes, which limits its applicatio…