Search Results for author: Youngjoon Yoo

Found 24 papers, 11 papers with code

Observations on K-image Expansion of Image-Mixing Augmentation for Classification

no code implementations8 Oct 2021 JoonHyun Jeong, Sungmin Cha, Youngjoon Yoo, Sangdoo Yun, Taesup Moon, Jongwon Choi

Image-mixing augmentations (e. g., Mixup or CutMix), which typically mix two images, have become de-facto training tricks for image classification.

Classification Image Classification

Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement

no code implementations20 Sep 2021 Beomyoung Kim, Youngjoon Yoo, Chaeeun Rhee, Junmo Kim

Second, we propose a self-refinement method that refines the pseudo instance labels in a self-supervised scheme and employs them to the training in an online manner while resolving the semantic drift problem.

Instance Segmentation Transfer Learning +2

Self-Supervised Iterative Contextual Smoothing for Efficient Adversarial Defense against Gray- and Black-Box Attack

no code implementations22 Jun 2021 Sungmin Cha, Naeun Ko, Youngjoon Yoo, Taesup Moon

We propose a novel and effective input transformation based adversarial defense method against gray- and black-box attack, which is computationally efficient and does not require any adversarial training or retraining of a classification model.

Adversarial Defense

Rainbow Memory: Continual Learning with a Memory of Diverse Samples

1 code implementation CVPR 2021 Jihwan Bang, Heesu Kim, Youngjoon Yoo, Jung-Woo Ha, Jonghyun Choi

Prevalent scenario of continual learning, however, assumes disjoint sets of classes as tasks and is less realistic rather artificial.

Continual Learning Data Augmentation

More than just an auxiliary loss: Anti-spoofing Backbone Training via Adversarial Pseudo-depth Generation

no code implementations1 Jan 2021 Chang Keun Paik, Naeun Ko, Youngjoon Yoo

In this paper, a new method of training pipeline is discussed to achieve significant performance on the task of anti-spoofing with RGB image.

Rethinking Channel Dimensions for Efficient Model Design

6 code implementations CVPR 2021 Dongyoon Han, Sangdoo Yun, Byeongho Heo, Youngjoon Yoo

We then investigate the channel configuration of a model by searching network architectures concerning the channel configuration under the computational cost restriction.

Instance Segmentation Object Detection +2

Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples

no code implementations19 Jun 2020 Jihwan Bang, Heesu Kim, Youngjoon Yoo, Jung-Woo Ha

The cost of annotating transcriptions for large speech corpora becomes a bottleneck to maximally enjoy the potential capacity of deep neural network-based automatic speech recognition models.

Active Learning automatic-speech-recognition +1

FrostNet: Towards Quantization-Aware Network Architecture Search

1 code implementation17 Jun 2020 Taehoon Kim, Youngjoon Yoo, Jihoon Yang

In this paper, we present a new network architecture search (NAS) procedure to find a network that guarantees both full-precision (FLOAT32) and quantized (INT8) performances.

Object Detection Quantization +1

An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods

no code implementations9 Mar 2020 Sanghyuk Chun, Seong Joon Oh, Sangdoo Yun, Dongyoon Han, Junsuk Choe, Youngjoon Yoo

Despite apparent human-level performances of deep neural networks (DNN), they behave fundamentally differently from humans.

Bayesian Inference

SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder

8 code implementations20 Nov 2019 Hyojin Park, Lars Lowe Sjösund, Youngjoon Yoo, Nicolas Monet, Jihwan Bang, Nojun Kwak

To solve the first problem, we introduce the new extremely lightweight portrait segmentation model SINet, containing an information blocking decoder and spatial squeeze modules.

Portrait Segmentation Semantic Segmentation

Neural Approximation of an Auto-Regressive Process through Confidence Guided Sampling

no code implementations15 Oct 2019 YoungJoon Yoo, Sanghyuk Chun, Sangdoo Yun, Jung-Woo Ha, Jaejun Yoo

We first assume that the priors of future samples can be generated in an independently and identically distributed (i. i. d.)

Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold

no code implementations CVPR 2017 YoungJoon Yoo, Sangdoo Yun, Hyung Jin Chang, Yiannis Demiris, Jin Young Choi

(iii) The proposed regression is embedded into a generative model, and the whole procedure is developed by the variational autoencoder framework.

ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modules

3 code implementations8 Aug 2019 Hyojin Park, Lars Lowe Sjösund, Youngjoon Yoo, Jihwan Bang, Nojun Kwak

In our qualitative and quantitative analysis on the EG1800 dataset, we show that our method outperforms various existing lightweight segmentation models.

Portrait Segmentation Semantic Segmentation

EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse

2 code implementations15 Jun 2019 YoungJoon Yoo, Dongyoon Han, Sangdoo Yun

In this paper, we propose a new multi-scale face detector having an extremely tiny number of parameters (EXTD), less than 0. 1 million, as well as achieving comparable performance to deep heavy detectors.

Face Detection

C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation

2 code implementations12 Dec 2018 Hyojin Park, Youngjoon Yoo, Geonseok Seo, Dongyoon Han, Sangdoo Yun, Nojun Kwak

To resolve this problem, we propose a new block called Concentrated-Comprehensive Convolution (C3) which applies the asymmetric convolutions before the depth-wise separable dilated convolution to compensate for the information loss due to dilated convolution.

Semantic Segmentation

MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis

1 code implementation3 May 2018 Hyojin Park, YoungJoon Yoo, Nojun Kwak

This block enables MC-GAN to generate a realistic object image with the desired background by controlling the amount of the background information from the given base image using the foreground information from the text attributes.

Text-to-Image Generation

Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation

no code implementations27 Nov 2017 YoungJoon Yoo, SeongUk Park, Junyoung Choi, Sangdoo Yun, Nojun Kwak

In addition to this performance enhancement problem, we show that the proposed PGN can be adopted to solve the classical adversarial problem without utilizing the information on the target classifier.

Classification General Classification

Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

no code implementations CVPR 2018 Daesik Kim, Youngjoon Yoo, Jeesoo Kim, Sangkuk Lee, Nojun Kwak

In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way.

Graph Generation Question Answering

BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning

no code implementations5 Sep 2017 Simyung Chang, Youngjoon Yoo, Jae-Seok Choi, Nojun Kwak

Our method learns hundreds to thousand times faster than the conventional methods by learning only a handful of core cluster information, which shows that deep RL agents can effectively learn through the shared knowledge from other agents.

Imitation Learning

Action-Decision Networks for Visual Tracking With Deep Reinforcement Learning

1 code implementation CVPR 2017 Sangdoo Yun, Jongwon Choi, Youngjoon Yoo, Kimin Yun, Jin Young Choi

In contrast to the existing trackers using deep networks, the proposed tracker is designed to achieve a light computation as well as satisfactory tracking accuracy in both location and scale.

Visual Tracking

Superpixel-based Semantic Segmentation Trained by Statistical Process Control

1 code implementation30 Jun 2017 Hyojin Park, Jisoo Jeong, Youngjoon Yoo, Nojun Kwak

Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks.

Semantic Segmentation

Visual Path Prediction in Complex Scenes With Crowded Moving Objects

no code implementations CVPR 2016 YoungJoon Yoo, Kimin Yun, Sangdoo Yun, JongHee Hong, Hawook Jeong, Jin Young Choi

In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects.

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