162 citations · 217 across the 6 of their papers we have counts for
5 papers · 1 filter
Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner
Chaehyeon Lee, Junghoon Seo, Heechul Jung
In deep learning-based object detection on remote sensing domain, nuisance factors, which affect observed variables while not affecting predictor variables, often matters because t…
FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation
Jaemin Na, Heechul Jung, Hyung Jin Chang +1
Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were based on direct adap…
Development of Fast Refinement Detectors on AI Edge Platforms
Min-Kook Choi, Heechul Jung
With the improvements in the object detection networks, several variations of object detection networks have been achieved impressive performance. However, the performance evaluati…
Co-occurrence matrix analysis-based semi-supervised training for object detection
Min-Kook Choi, Jaehyeong Park, Jihun Jung +6
One of the most important factors in training object recognition networks using convolutional neural networks (CNNs) is the provision of annotated data accompanying human judgment.…
Deep Temporal Appearance-Geometry Network for Facial Expression Recognition
Heechul Jung, Sihaeng Lee, Sunjeong Park +3
Temporal information can provide useful features for recognizing facial expressions. However, to manually design useful features requires a lot of effort. In this paper, to reduce…