Computer Vision

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http://data.inu.ac.kr/handle/123456789/1932

About this Collection

이 컬렉션은 Computer Vision (CV) 분야에서 인공지능 기술이 어떻게 발전해왔는지를 보여주는 핵심 논문들을 담고 있습니다. CNN 기반 AlexNet, ResNet, U-Net부터 Transformer 구조의 ViT, 생성 모델 StyleGAN, 객체 탐지 YOLO까지 포함됩니다. 또한 의료 진단, 산불 예측 등 실제 응용 사례를 다룬 논문도 수록되어 있으며, 실세계 문제 해결에 있어 컴퓨터 비전 기술이 어떻게 활용되는지를 보여줍니다.

포함된 논문 유형 요약

기초 모델: AlexNet, ResNet, U-Net, FCN
최신 구조: Vision Transformer (ViT), StyleGAN
의료/환경 응용: Skin Cancer Classification, Wildfire Simulation
객체 탐지: YOLO
준지도학습: FixMatch

주요 논문

Krizhevsky et al. – ImageNet Classification with Deep CNN (AlexNet)
He et al. – Deep Residual Learning for Image Recognition (ResNet)
Ronneberger et al. – U-Net for Biomedical Image Segmentation
Dosovitskiy et al. – Vision Transformer (ViT)
Karras et al. – StyleGAN

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Now showing 1 - 3 of 3

Deep Residual Learning for Image Recognition
(arXiv, 2015-12-10) He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Dermatologist-level classification of skin cancer with deep neural networks
(Nature, 2017-01-25) Esteva, Andre; Kuprel, Brett; Novoa, Roberto A; Ko, Justin; Swetter, Susan M; Blau, Helen M; Thrun, Sebastian
Skin cancer, the most common human malignancy1,2,3, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs)4,5 show potential for general and highly variable tasks across many fine-grained object categories6,7,8,9,10,11. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets12—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
Fully Convolutional Networks for Semantic Segmentation
(arXiv, 2015-11-14) Long, Jonathan; Shelhamer, Evan; Darrell, Trevor
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a novel architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes one third of a second for a typical image.

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Browsing Computer Vision by Subject "Deep Learning"

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