21 citations · 25 across the 8 of their papers we have counts for
9 papers
Learning Question-Aware Keyframe Selection with Synthetic Supervision for Video Question Answering
Minchan Kwon, Hyounguk Shon, Junmo Kim
Large multimodal models (LMMs) have recently demonstrated remarkable performance in video question answering (VideoQA), yet reasoning over video remains challenging due to high inf…
DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization
Dongyeun Lee, Jiwan Hur, Hyounguk Shon +2
Diffusion models have achieved remarkable success in image generation but come with significant computational costs, posing challenges for deployment in resource-constrained enviro…
SFLD: Reducing the content bias for AI-generated Image Detection
Seoyeon Gye, Junwon Ko, Hyounguk Shon +2
Identifying AI-generated content is critical for the safe and ethical use of generative AI. Recent research has focused on developing detectors that generalize to unknown generator…
FRED: Towards a Full Rotation-Equivariance in Aerial Image Object Detection
Chanho Lee, Jinsu Son, Hyounguk Shon +2
Rotation-equivariance is an essential yet challenging property in oriented object detection. While general object detectors naturally leverage robustness to spatial shifts due to t…
Disposable Transfer Learning for Selective Source Task Unlearning
Seunghee Koh, Hyounguk Shon, Janghyeon Lee +2
Transfer learning is widely used for training deep neural networks (DNN) for building a powerful representation. Even after the pre-trained model is adapted for the target task, th…
Lightweight Monocular Depth Estimation via Token-Sharing Transformer
Dong-Jae Lee, Jae Young Lee, Hyounguk Shon +4
Depth estimation is an important task in various robotics systems and applications. In mobile robotics systems, monocular depth estimation is desirable since a single RGB camera ca…
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training
Janghyeon Lee, Jongsuk Kim, Hyounguk Shon +4
Pre-training vision-language models with contrastive objectives has shown promising results that are both scalable to large uncurated datasets and transferable to many downstream a…
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning
Hyounguk Shon, Janghyeon Lee, Seung Hwan Kim +1
Pre-trained representation is one of the key elements in the success of modern deep learning. However, existing works on continual learning methods have mostly focused on learning…
Joint Negative and Positive Learning for Noisy Labels
Youngdong Kim, Juseung Yun, Hyounguk Shon +1
Training of Convolutional Neural Networks (CNNs) with data with noisy labels is known to be a challenge. Based on the fact that directly providing the label to the data (Positive L…