• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5546-5559
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• 2019

Facial action units (AUs) have recently drawn increased attention because they can be used to recognize facial expressions. A variety of methods have been designed for frontal-view AU detection, but few have been able to handle multi-view face images. In this paper we propose a method for multi-view facial AU detection using a fused multilayer, multi-task, and multi-label deep learning network. The network can complete two tasks: AU detection and facial view detection. AU detection is a multi-label problem and facial view detection is a single-label problem. A residual network and multilayer fusion are applied to obtain more representative features. Our method is effective and performs well. The F1 score on FERA 2017 is 13.1% higher than the baseline. The facial view recognition accuracy is 0.991. This shows that our multi-task, multi-label model could achieve good performance on the two tasks.

• Smart Structures and Systems
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• 제31권4호
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

• 대한영상의학회지
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• 제83권6호
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• pp.1208-1218
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• 2022

최근, 의료 분야에서 인공지능은 많은 발전을 통해 다양한 분야로 확장하며 활용되고 있다. 하지만 대부분의 인공지능 기술들은 하나의 모델이 하나의 태스크만을 수행할 수 있도록 개발되고 있으며, 이는 의사들의 복잡한 판독 과정을 인공지능으로 설계하는데 한계로 작용한다. 멀티 태스크 러닝은 이러한 한계를 극복하기 위한 최적의 방안으로 알려져 있다. 다양한 태스크들을 동시에 하나의 모델로 학습함으로써, 효율적이고 일반화에 유리한 모델을 만들수 있다. 본 종설에서는 멀티 태스크 러닝에 대한 개념과 종류, 유사 개념 등에 대해 알아보고, 연구 사례들을 통해 의료 분야에서의 멀티 태스크 러닝의 활용 현황과 향후 가능성을 살펴보고자 한다.

• ETRI Journal
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• 제46권1호
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• pp.82-95
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• 2024

As automated essay scoring (AES) has progressed from handcrafted techniques to deep learning, holistic scoring capabilities have merged. However, specific trait assessment remains a challenge because of the limited depth of earlier methods in modeling dual assessments for holistic and multi-trait tasks. To overcome this challenge, we explore providing comprehensive feedback while modeling the interconnections between holistic and trait representations. We introduce the DualBERT-Trans-CNN model, which combines transformer-based representations with a novel dual-scale bidirectional encoder representations from transformers (BERT) encoding approach at the document-level. By explicitly leveraging multi-trait representations in a multi-task learning (MTL) framework, our DualBERT-Trans-CNN emphasizes the interrelation between holistic and trait-based score predictions, aiming for improved accuracy. For validation, we conducted extensive tests on the ASAP++ and TOEFL11 datasets. Against models of the same MTL setting, ours showed a 2.0% increase in its holistic score. Additionally, compared with single-task learning (STL) models, ours demonstrated a 3.6% enhancement in average multi-trait performance on the ASAP++ dataset.

• 한국멀티미디어학회논문지
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• 제22권9호
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• pp.1000-1010
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• 2019

Image semantic segmentation and dehazing are key tasks in the computer vision. In recent years, researches in both tasks have achieved substantial improvements in performance with the development of Convolutional Neural Network (CNN). However, most of the previous works for semantic segmentation assume the images are captured in clear weather and show degraded performance under hazy images with low contrast and faded color. Meanwhile, dehazing aims to recover clear image given observed hazy image, which is an ill-posed problem and can be alleviated with additional information about the image. In this work, we propose a deep multi-task network for simultaneous semantic segmentation and dehazing. The proposed network takes single haze image as input and predicts dense semantic segmentation map and clear image. The visual information getting refined during the dehazing process can help the recognition task of semantic segmentation. On the other hand, semantic features obtained during the semantic segmentation process can provide cues for color priors for objects, which can help dehazing process. Experimental results demonstrate the effectiveness of the proposed multi-task approach, showing improved performance compared to the separate networks.

• 한국음향학회지
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• 제38권5호
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• pp.593-600
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• 2019

화자 간 음색의 유사성은 화자 인증 시스템의 성능을 하락 시킬 수 있는 요인이다. 본 논문은 화자 인증 시스템의 일반화 성능을 향상시키기 위해, 심층신경망에 멀티태스크 러닝 기법을 적용시켜 발화자의 화자 정보와 나이 정보를 함께 학습 시키는 기법을 제안한다. 멀티태스크 러닝 기법은 은닉층들이 하나의 태스크에 과적합 되지 않도록 하여 심층신경망의 일반화 성능을 향상시킨다고 알려져 있다. 하지만 심층신경망을 멀티태스크 러닝 기법으로 학습시키는 과정에서, 나이 정보에 대한 학습이 효율적으로 수행되지 않는 것을 실험적으로 확인하였다. 이와 같은 현상을 방지하기 위해, 본 논문에서는 심층신경망의 학습 과정 중 화자 식별과 나이 추정 목적 함수의 가중치를 동적으로 변경 하는 기법을 제안한다. 동일 오류율을 기준으로 RSR2015 평가 데이터세트에 대해 화자 인증 성능을 평가한 결과 나이 정보를 활용하지 않은 화자 인증 시스템의 경우 6.91 %, 나이 정보를 활용한 화자 인증 시스템의 경우 6.77 %, 나이 정보를 활용한 화자 인증 시스템에 가중치 변경 기법을 적용한 경우 4.73 %의 오류율을 확인하였다.

• 한국멀티미디어학회논문지
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• 제22권10호
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• pp.1149-1159
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• 2019

We present a novel deep learning architecture for obtaining a latent image from a single blurry image, which contains dynamic motion blurs through object/camera movements. The proposed architecture consists of two sub-modules: blur image restoration and optical flow estimation. The tasks are highly related in that object/camera movements make cause blurry artifacts, whereas they are estimated through optical flow. The ablation study demonstrates that training multi-task architecture simultaneously improves both tasks compared to handling them separately. Objective and subjective evaluations show that our method outperforms the state-of-the-arts deep learning based techniques.

• 정보과학회 논문지
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• 제44권8호
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• pp.822-831
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• 2017

본 논문에서는 멀티 태스크 학습 기반 포인터 네트워크를 이용한 한국어 의존 구문 분석 모델을 제안한다. 멀티 태스크 학습은 두 개 이상의 문제를 동시에 학습시켜 성능을 향상시키는 방법으로, 본 논문에서는 이 방법에 기반한 포인터 네트워크를 이용하여 어절 간의 의존 관계와 의존 레이블 정보를 동시에 구하여 의존 구문 분석을 수행한다. 어절 기반의 의존 구문 분석에서 형태소 기반의 멀티 태스크 학습 기반 포인터 네트워크를 수행하기 위하여 입력 기준 5가지를 정의하고, 성능 향상을 위하여 fine-tuning 방법을 적용한다. 실험 결과, 본 논문에서 제안한 모델이 기존 한국어 의존 구문 분석 연구들 보다 좋은 UAS 91.79%, LAS 89.48%의 성능을 보였다.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1310-1319
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• 2016

Face detection is the first step in a wide range of face applications. However, detecting faces in the wild is still a challenging task due to the wide range of variations in pose, scale, and occlusions. Recently, many deep learning methods have been proposed for face detection. However, further improvements are required in the wild. Another important issue to be considered in the face detection is the computational complexity. Current state-of-the-art deep learning methods require a large number of patches to deal with varying scales and the arbitrary image sizes, which result in an increased computational complexity. To reduce the complexity while achieving better detection accuracy, we propose a fully convolutional network-based face detection that can take arbitrarily-sized input and produce feature maps (heat maps) corresponding to the input image size. To deal with the various face scales, a multi-scale network architecture that utilizes the facial components when learning the feature maps is proposed. On top of it, we design multi-task learning technique to improve detection performance. Extensive experiments have been conducted on the FDDB dataset. The experimental results show that the proposed method outperforms state-of-the-art methods with the accuracy of 82.33% at 517 false alarms, while improving computational efficiency significantly.

• International Journal of Internet, Broadcasting and Communication
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• 제16권3호
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• pp.192-198
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• 2024

This paper investigates the application of multi-agent deep reinforcement learning in the fighting game Samurai Shodown using Proximal Policy Optimization (PPO) and Advantage Actor-Critic (A2C) algorithms. Initially, agents are trained separately for 200,000 timesteps using Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) with LSTM networks. PPO demonstrates superior performance early on with stable policy updates, while A2C shows better adaptation and higher rewards over extended training periods, culminating in A2C outperforming PPO after 1,000,000 timesteps. These findings highlight PPO's effectiveness for short-term training and A2C's advantages in long-term learning scenarios, emphasizing the importance of algorithm selection based on training duration and task complexity. The code can be found in this link https://github.com/Lexer04/Samurai-Shodown-with-Reinforcement-Learning-PPO.