6 citations · 21 across the 11 of their papers we have counts for
6 papers · 1 filter
Single-Modal Entropy based Active Learning for Visual Question Answering
Dong-Jin Kim, Jae Won Cho, Jinsoo Choi +2
Constructing a large-scale labeled dataset in the real world, especially for high-level tasks (eg, Visual Question Answering), can be expensive and time-consuming. In addition, wit…
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation
Antyanta Bangunharcana, Jae Won Cho, Seokju Lee +3
Volumetric deep learning approach towards stereo matching aggregates a cost volume computed from input left and right images using 3D convolutions. Recent works showed that utiliza…
LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation
Inkyu Shin, Dong-jin Kim, Jae Won Cho +3
Unsupervised Domain Adaptation (UDA) for semantic segmentation has been actively studied to mitigate the domain gap between label-rich source data and unlabeled target data. Despit…
MCDAL: Maximum Classifier Discrepancy for Active Learning
Jae Won Cho, Dong-Jin Kim, Yunjae Jung +1
Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition; however, GAN is usually known to suffer from ins…
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation
Jae Won Cho, Dong-Jin Kim, Jinsoo Choi +2
In this work, we address the issues of missing modalities that have arisen from the Visual Question Answer-Difference prediction task and find a novel method to solve the task at h…
Optical Flow Estimation from a Single Motion-blurred Image
Dawit Mureja Argaw, Junsik Kim, Francois Rameau +2
In most of computer vision applications, motion blur is regarded as an undesirable artifact. However, it has been shown that motion blur in an image may have practical interests in…