6 papers
Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation
Duy Tran Thanh, Yeejin Lee, Byeongkeun Kang
This work addresses the challenge of open-vocabulary instance segmentation (OVIS) and open-set panoptic segmentation (OSPS), which aim to recognize both predefined and unseen objec…
Generalized Zero-Shot Learning for Point Cloud Segmentation with Evidence-Based Dynamic Calibration
Hyeonseok Kim, Byeongkeun Kang, Yeejin Lee
Generalized zero-shot semantic segmentation of 3D point clouds aims to classify each point into both seen and unseen classes. A significant challenge with these models is their ten…
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…
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…
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…
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…