46 citations · 53 across the 10 of their papers we have counts for
11 papers · 1 filter
1st Place Solution to MultiEarth 2023 Challenge on Multimodal SAR-to-EO Image Translation
Jingi Ju, Hyeoncheol Noh, Minwoo Kim +1
The Multimodal Learning for Earth and Environment Workshop (MultiEarth 2023) aims to harness the substantial amount of remote sensing data gathered over extensive periods for the m…
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…
CAFS: Class Adaptive Framework for Semi-Supervised Semantic Segmentation
Jingi Ju, Hyeoncheol Noh, Yooseung Wang +2
Semi-supervised semantic segmentation learns a model for classifying pixels into specific classes using a few labeled samples and numerous unlabeled images. The recent leading appr…
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…
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…
Unsupervised Change Detection Based on Image Reconstruction Loss
Hyeoncheol Noh, Jingi Ju, Minseok Seo +2
To train the change detector, bi-temporal images taken at different times in the same area are used. However, collecting labeled bi-temporal images is expensive and time consuming.…
PT4AL: Using Self-Supervised Pretext Tasks for Active Learning
John Seon Keun Yi, Minseok Seo, Jongchan Park +1
Labeling a large set of data is expensive. Active learning aims to tackle this problem by asking to annotate only the most informative data from the unlabeled set. We propose a nov…
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…
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms
Inwook Shim, Tae-Hyun Oh, Joon-Young Lee +3
We introduce a novel method to automatically adjust camera exposure for image processing and computer vision applications on mobile robot platforms. Because most image processing a…
Vision System and Depth Processing for DRC-HUBO+
Inwook Shim, Seunghak Shin, Yunsu Bok +6
This paper presents a vision system and a depth processing algorithm for DRC-HUBO+, the winner of the DRC finals 2015. Our system is designed to reliably capture 3D information of…