5 papers
Multispectral Pedestrian Detection with Sparsely Annotated Label
Chan Lee, Seungho Shin, Gyeong-Moon Park +1
Although existing Sparsely Annotated Object Detection (SAOD) approches have made progress in handling sparsely annotated environments in multispectral domain, where only some pedes…
Towards Model-Agnostic Dataset Condensation by Heterogeneous Models
Jun-Yeong Moon, Jung Uk Kim, Gyeong-Moon Park
Abstract. The advancement of deep learning has coincided with the proliferation of both models and available data. The surge in dataset sizes and the subsequent surge in computatio…
Versatile Incremental Learning: Towards Class and Domain-Agnostic Incremental Learning
Min-Yeong Park, Jae-Ho Lee, Gyeong-Moon Park
Incremental Learning (IL) aims to accumulate knowledge from sequential input tasks while overcoming catastrophic forgetting. Existing IL methods typically assume that an incoming t…
GLAD: Global-Local View Alignment and Background Debiasing for Unsupervised Video Domain Adaptation with Large Domain Gap
Hyogun Lee, Kyungho Bae, Seong Jong Ha +3
In this work, we tackle the challenging problem of unsupervised video domain adaptation (UVDA) for action recognition. We specifically focus on scenarios with a substantial domain…
Continual Unsupervised Domain Adaptation for Semantic Segmentation
Joonhyuk Kim, Sahng-Min Yoo, Gyeong-Moon Park +1
Unsupervised Domain Adaptation (UDA) for semantic segmentation has been favorably applied to real-world scenarios in which pixel-level labels are hard to be obtained. In most of th…