32 citations · 86 across the 10 of their papers we have counts for
11 papers
Feature Diversification and Adaptation for Federated Domain Generalization
Seunghan Yang, Seokeon Choi, Hyunsin Park +3
Federated learning, a distributed learning paradigm, utilizes multiple clients to build a robust global model. In real-world applications, local clients often operate within their…
Progressive Random Convolutions for Single Domain Generalization
Seokeon Choi, Debasmit Das, Sungha Choi +3
Single domain generalization aims to train a generalizable model with only one source domain to perform well on arbitrary unseen target domains. Image augmentation based on Random…
Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes
Sungha Choi, Seunghan Yang, Seokeon Choi +1
This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained on the source domain using only unlabeled online data from the target domain to allevia…
Learning to Discriminate Information for Online Action Detection: Analysis and Application
Sumin Lee, Hyunjun Eun, Jinyoung Moon +4
Online action detection, which aims to identify an ongoing action from a streaming video, is an important subject in real-world applications. For this task, previous methods use re…
Few-shot Open-set Recognition by Transformation Consistency
Minki Jeong, Seokeon Choi, Changick Kim
In this paper, we attack a few-shot open-set recognition (FSOSR) problem, which is a combination of few-shot learning (FSL) and open-set recognition (OSR). It aims to quickly adapt…
Meta Batch-Instance Normalization for Generalizable Person Re-Identification
Seokeon Choi, Taekyung Kim, Minki Jeong +2
Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, gene…
Robust Long-Term Object Tracking via Improved Discriminative Model Prediction
Seokeon Choi, Junhyun Lee, Yunsung Lee +1
We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker. The baseline pre-trained short-term tracker i…
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification
Seokeon Choi, Sumin Lee, Youngeun Kim +2
Visible-infrared person re-identification (VI-ReID) is an important task in night-time surveillance applications, since visible cameras are difficult to capture valid appearance in…
RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised Video Object Segmentation
Youngeun Kim, Seokeon Choi, Hankyeol Lee +2
In this paper, we introduce a self-supervised approach for video object segmentation without human labeled data.Specifically, we present Robust Pixel-level Matching Net-works (RPM-…
Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification
Youngeun Kim, Seokeon Choi, Taekyung Kim +2
Most video person re-identification (re-ID) methods are mainly based on supervised learning, which requires cross-camera ID labeling. Since the cost of labeling increases dramatica…
Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection
Taekyung Kim, Minki Jeong, Seunghyeon Kim +2
We introduce a novel unsupervised domain adaptation approach for object detection. We aim to alleviate the imperfect translation problem of pixel-level adaptations, and the source-…