5 papers
Detecting Unknown Objects via Energy-based Separation for Open World Object Detection
Jun-Woo Heo, Keonhee Park, Gyeong-Moon Park
In this work, we tackle the problem of Open World Object Detection (OWOD). This challenging scenario requires the detector to incrementally learn to classify known objects without…
Universal Domain Adaptation for Semantic Segmentation
Seun-An Choe, Keon-Hee Park, Jinwoo Choi +1
Unsupervised domain adaptation for semantic segmentation (UDA-SS) aims to transfer knowledge from labeled source data to unlabeled target data. However, traditional UDA-SS methods…
Online Continuous Generalized Category Discovery
Keon-Hee Park, Hakyung Lee, Kyungwoo Song +1
With the advancement of deep neural networks in computer vision, artificial intelligence (AI) is widely employed in real-world applications. However, AI still faces limitations in…
Open-Set Domain Adaptation for Semantic Segmentation
Seun-An Choe, Ah-Hyung Shin, Keon-Hee Park +2
Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer the pixel-wise knowledge from the labeled source domain to the unlabeled target domain. However, cur…
Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners
Keon-Hee Park, Kyungwoo Song, Gyeong-Moon Park
Few-Shot Class Incremental Learning (FSCIL) is a task that requires a model to learn new classes incrementally without forgetting when only a few samples for each class are given.…