no code implementations • ICCV 2023 • Sunghyun Park, Seunghan Yang, Jaegul Choo, Sungrack Yun
Test-time adaptation (TTA) aims to adapt a pre-trained model to the target domain in a batch-by-batch manner during inference.
no code implementations • CVPR 2023 • Seokeon Choi, Debasmit Das, Sungha Choi, Seunghan Yang, Hyunsin Park, Sungrack Yun
Single domain generalization aims to train a generalizable model with only one source domain to perform well on arbitrary unseen target domains.
no code implementations • ICCV 2023 • JunTae Lee, Mihir Jain, Sungrack Yun
In this task, it is crucial to mine reliable temporal cues representing a common action from handful support videos.
no code implementations • 24 Jul 2022 • Sungha Choi, Seunghan Yang, Seokeon Choi, Sungrack Yun
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 alleviate the performance degradation due to the distribution shift between the source and target domains.
no code implementations • 28 Jun 2022 • Seunghan Yang, Debasmit Das, Janghoon Cho, Hyoungwoo Park, Sungrack Yun
Deep learning models for verification systems often fail to generalize to new users and new environments, even though they learn highly discriminative features.
no code implementations • 24 Nov 2021 • Seunghan Yang, Debasmit Das, Simyung Chang, Sungrack Yun, Fatih Porikli
However, it is observed that image transformations already present in the dataset might be less effective in learning such self-supervised representations.
no code implementations • ICLR 2022 • Debasmit Das, Sungrack Yun, Fatih Porikli
The first step of our framework trains a feature extracting backbone with the contrastive loss on the base category data.
no code implementations • 18 Apr 2021 • Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling
We consider the problem of training User Verification (UV) models in federated setting, where each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.
no code implementations • 25 Mar 2021 • Jangho Kim, Simyung Chang, Sungrack Yun, Nojun Kwak
We verify the usefulness of PPP on a couple of tasks in computer vision and Keyword spotting.
no code implementations • 25 Mar 2021 • Simyung Chang, Hyoungwoo Park, Janghoon Cho, Hyunsin Park, Sungrack Yun, Kyuwoong Hwang
In this work, we introduce SubSpectral Normalization (SSN), which splits the input frequency dimension into several groups (sub-bands) and performs a different normalization for each group.
Ranked #1 on Keyword Spotting on TAU Urban Acoustic Scenes 2019
no code implementations • 1 Jan 2021 • Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling
We consider the problem of training User Verification (UV) models in federated setup, where the conventional loss functions are not applicable due to the constraints that each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.
no code implementations • ICCV 2021 • HanUl Kim, Mihir Jain, Jun-Tae Lee, Sungrack Yun, Fatih Porikli
Efficient action recognition has become crucial to extend the success of action recognition to many real-world applications.
no code implementations • ICLR 2021 • Jun-Tae Lee, Mihir Jain, Hyoungwoo Park, Sungrack Yun
Temporally localizing actions in videos is one of the key components for video understanding.
no code implementations • 9 Jul 2020 • Hossein Hosseini, Sungrack Yun, Hyunsin Park, Christos Louizos, Joseph Soriaga, Max Welling
In this paper, we propose Federated User Authentication (FedUA), a framework for privacy-preserving training of UA models.
1 code implementation • 6 May 2020 • Seungwoo Yoo, Heeseok Lee, Heesoo Myeong, Sungrack Yun, Hyoungwoo Park, Janghoon Cho, Duck Hoon Kim
In autonomous driving, detecting reliable and accurate lane marker positions is a crucial yet challenging task.
Ranked #19 on Lane Detection on TuSimple
no code implementations • 14 Oct 2019 • Janghoon Cho, Sungrack Yun, Hyoungwoo Park, Jungyun Eum, Kyuwoong Hwang
With this loss function, the samples from the same audio scene are clustered independently of the environment, and thus we can get the classifier with better generalization ability in an unseen environment.
no code implementations • 14 Oct 2019 • Hyoungwoo Park, Sungrack Yun, Jungyun Eum, Janghoon Cho, Kyuwoong Hwang
This paper considers a semi-supervised learning framework for weakly labeled polyphonic sound event detection problems for the DCASE 2019 challenge's task4 by combining both the tri-training and adversarial learning.
no code implementations • 6 Aug 2019 • Sungrack Yun, Janghoon Cho, Jungyun Eum, Wonil Chang, Kyuwoong Hwang
In training our speaker verification framework, we consider both the triplet loss minimization and adversarial gradient of the ASR network to obtain more discriminative and text-independent speaker embedding vectors.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • NeurIPS 2012 • Hyunsin Park, Sungrack Yun, Sanghyuk Park, Jongmin Kim, Chang D. Yoo
This paper describes a new acoustic model based on variational Gaussian process dynamical system (VGPDS) for phoneme classification.