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https://arxiv.org/abs/1808.06601 Video-to-Video Synthesis We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source vide arxiv.org abstract 비디오 - 비디오 합성에 대한 문제를 연구한다. 기존 비디오의 내용을 정확하게 묘사하여 사질적인 비디오로 만드는 것..

https://www.researchgate.net/publication/258400466_Enhanced_Cognitive_Walkthrough_Development_of_the_Cognitive_Walkthrough_Method_to_Better_Predict_Identify_and_Present_Usability_Problems (PDF) Enhanced Cognitive Walkthrough: Development of the Cognitive Walkthrough Method to Better Predict, Identify, and Present U PDF | To avoid use errors when handling medical equipment, it is important to dev..

https://arxiv.org/abs/1905.11946v3 EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing n arxiv.org abstract CNN의 경우 제한된 자원..

https://arxiv.org/abs/1708.02002 Focal Loss for Dense Object Detection The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampl arxiv.org abstract 높은 정확도의 object detector는 two stage 방법으로 부족한 객체에 대한 ..

https://arxiv.org/abs/2004.04931v2 CoroNet: A Deep Neural Network for Detection and Diagnosis of Covid-19 from Chest X-ray Images The novel Coronavirus also called Covid-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114698 lives overall. As the number of cases are rapidly arxiv.org ab..

https://arxiv.org/abs/1606.04080 Matching Networks for One Shot Learning Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new conce arxiv.org abstract

https://arxiv.org/abs/1905.01436 Edge-labeling Graph Neural Network for Few-shot Learning In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous graph neural network (GNN) approaches in few-shot learning have been based on th arxiv.org abstract

https://arxiv.org/abs/2003.06957v1 Frustratingly Simple Few-Shot Object Detection Detecting rare objects from a few examples is an emerging problem. Prior works show meta-learning is a promising approach. But, fine-tuning techniques have drawn scant attention. We find that fine-tuning only the last layer of existing detectors on rare cl arxiv.org abstract 적은 예시에서 희귀한 물체를 찾아내는것이 대두되고 있다. 선행 연구에 따..

https://arxiv.org/abs/1909.11862v2 Convolutional Neural Networks with Dynamic Regularization Regularization is commonly used for alleviating overfitting in machine learning. For convolutional neural networks (CNNs), regularization methods, such as DropBlock and Shake-Shake, have illustrated the improvement in the generalization performance. Howeve arxiv.org abstract 본 논문은 CNN을 위한 동적 정규화(Regulari..

https://arxiv.org/abs/2003.12047v1 Learning Inverse Rendering of Faces from Real-world Videos In this paper we examine the problem of inverse rendering of real face images. Existing methods decompose a face image into three components (albedo, normal, and illumination) by supervised training on synthetic face data. However, due to the domain gap be arxiv.org abstract 본 논문은 실제 얼굴 이미지의 역 렌더링 문제를 검..