Computer Vision111 [2025-1] 황영희 - U-Net: Convolutional Networks for Biomedical Image Segmentation https://arxiv.org/abs/1505.04597 U-Net: Convolutional Networks for Biomedical Image SegmentationThere is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotatedarxiv.org1. U-Net 이란?이미지 세그멘테이션(Image Segmenta.. 2025. 2. 13. [2025-1] 황징아이 - Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation 논문 : https://arxiv.org/abs/2203.15227코드 : https://github.com/Pose-Group/FAMI-Pose GitHub - Pose-Group/FAMI-Pose: This is an official implementation of our CVPR 2022 ORAL paper "Temporal Feature Alignment and MuThis is an official implementation of our CVPR 2022 ORAL paper "Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation" . - Pose-Group/FAMI-Po.. 2025. 2. 8. [2025-1] 유경석 - MAISI: Medical AI for Synthetic Imaging https://arxiv.org/pdf/2409.11169v2 https://build.nvidia.com/nvidia/maisi maisi Model by NVIDIA | NVIDIA NIMMAISI is a pre-trained volumetric (3D) CT Latent Diffusion Generative Model.build.nvidia.com AbstractMAISI (Medical AI for Synthetic Imaging) : 3D 컴퓨터 단층촬영 (CT) 이미지 생성 모델Volume Compression Network : 고해상도 CT 이미지 생성Latent diffusion model : flexible volume dimensions과 voxel spacing 제공ControlNe.. 2025. 2. 8. [2025-1] 김유현 - A Style-Based Generator Architecture for Generative Adversarial Networks https://arxiv.org/abs/1812.04948 A Style-Based Generator Architecture for Generative Adversarial NetworksWe propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identitarxiv.org 0. AbstractStyleGAN은 스타일 전.. 2025. 2. 8. 이전 1 ··· 5 6 7 8 9 10 11 ··· 28 다음
