Computer Vision133 [2023-2] 양소정 - U-Net: Convolutional Networks for Biomedical Image Segmentation https://arxiv.org/pdf/1505.04597.pdf Abstract사용 가능한 주석이 달린 샘플을 보다 효율적으로 사용하기 위해, 하나의 데이터를 여러 데이터처럼 사용하는 전략(data augmentation)을 제시함정확한 localization을 가능하게 하는 대칭 확장 path로 구성됨이러한 네트워크는 적은 수의 이미지에서 end-to-end로 학습될 수 있음그 결과, 성능은 ISBI challenge for segmentation of neuronal structures in electron microscopic stacks에서 이전 최고 방법(a sliding-window convolutional network)을 능가함 ExtraConvolutional Neural Network.. 2024. 1. 8. [2023-2] 염제원 - TASK2VEC: Task Embedding for Meta-Learning https://arxiv.org/abs/1902.03545 Task2Vec: Task Embedding for Meta-Learning We introduce a method to provide vectorial representations of visual classification tasks which can be used to reason about the nature of those tasks and their relations. Given a dataset with ground-truth labels and a loss function defined over those label arxiv.org Abstract Visual Classification Task에서 Task를 Vector로 표현하.. 2024. 1. 7. [2023-2] 주서영 - Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an ImageNet-trained convolutional neural network. However, the recent past has arxiv.org Abstract neural network 기반의 com.. 2024. 1. 2. [2023-2] 현시은 - 3D image reconstruction from 2D CT slices(3DTV-CON2014) 원본 논문 링크 : https://ieeexplore.ieee.org/document/6874742 3D image reconstruction from 2D CT slicesIn this paper, a 3D reconstruction algorithm using CT slices of human pelvis is presented. We propose the method for 3D image reconstruction that is based on a combination of the SURF (Speeded-Up Robust Features) descriptor and SSD (Sum of Squared Differenieeexplore.ieee.orgAbstract이 논문에서는 정확한 인체 골반의.. 2024. 1. 2. 이전 1 ··· 29 30 31 32 33 34 다음
