2 citations · 2 across the 6 of their papers we have counts for
6 papers
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
Dina Bashkirova, Samarth Mishra, Diala Lteif +16
Label-efficient and reliable semantic segmentation is essential for many real-life applications, especially for industrial settings with high visual diversity, such as waste sortin…
Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation
Daehan Kim, Minseok Seo, Kwanyong Park +4
Mixup provides interpolated training samples and allows the model to obtain smoother decision boundaries for better generalization. The idea can be naturally applied to the domain…
1st Place Solution to NeurIPS 2022 Challenge on Visual Domain Adaptation
Daehan Kim, Minseok Seo, YoungJin Jeon +1
The Visual Domain Adaptation(VisDA) 2022 Challenge calls for an unsupervised domain adaptive model in semantic segmentation tasks for industrial waste sorting. In this paper, we in…
Source Domain Subset Sampling for Semi-Supervised Domain Adaptation in Semantic Segmentation
Daehan Kim, Minseok Seo, Jinsun Park +1
In this paper, we introduce source domain subset sampling (SDSS) as a new perspective of semi-supervised domain adaptation. We propose domain adaptation by sampling and exploiting…
Exploiting Features with Split-and-Share Module
Jaemin Lee, Minseok Seo, Jongchan Park +1
Deep convolutional neural networks (CNNs) have shown state-of-the-art performances in various computer vision tasks. Advances on CNN architectures have focused mainly on designing…
Sequential Feature Filtering Classifier
Minseok Seo, Jaemin Lee, Jongchan Park +1
We propose Sequential Feature Filtering Classifier (FFC), a simple but effective classifier for convolutional neural networks (CNNs). With sequential LayerNorm and ReLU, FFC zeroes…