AI (108) 썸네일형 리스트형 [VDE 논문 리뷰] - Vehicle Distance Estimation from a Monocular Camera for Advanced Driver Assistance Systems *VDE 논문 리뷰를 위한 글 입니다! 궁금하신 점이 있다면 댓글로 달아주세요! *이 논문에서는 Self-driving car에서 Distance estimation을 수행하는 딥러닝을 제시합니다. VDE(ODD) paper: https://www.mdpi.com/2073-8994/14/12/2657 Vehicle Distance Estimation from a Monocular Camera for Advanced Driver Assistance Systems The purpose of this study is to propose a framework for accurate and efficient vehicle distance estimation from a monocular camera. The pr.. [GLPDepth 논문 리뷰] - Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth *GLPDepth 논문 리뷰를 위한 글입니다! 궁금한 점이 있다면 댓글로 질문주세요! GLPDepth paper: [2201.07436] Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth (arxiv.org) Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth Depth estimation from a single image is an important task that can be applied to various fields in computer vision, and has grown rapidly with the .. [DETR 논문 리뷰] - End-to-End Object Detection with Transformers *DETR 논문 리뷰를 위한 글입니다! 궁금하신 점이 있다면 댓글로 남겨주세요. DETR paper: [2005.12872] End-to-End Object Detection with Transformers (arxiv.org) End-to-End Object Detection with Transformers We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum supp.. [NeRF-Art 논문 리뷰] - Text-Driven Neural Radiance Fields Stylization *NeRF-Art 논문 리뷰 글입니다! 궁금하신 점이 있다면 댓글로 남겨주세요! NeRF-Art paper: [2212.08070] NeRF-Art: Text-Driven Neural Radiance Fields Stylization (arxiv.org) NeRF-Art: Text-Driven Neural Radiance Fields Stylization As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images. Stylizing NeRF, however, remains challenging, especially.. [NeRF++ 논문 리뷰] - NERF++: ANALYZING AND IMPROVING NEURAL RADIANCE FIELDS *NeRF++ 논문 리뷰 글입니다! 질문 사항이 있다면 댓글로 남겨주시길 바랍니다. *기본적으로 난이도가 있는 논문이기에, NeRF를 이해하지 못하셨다면 힘드실 것으로 예상됩니다. NeRF++ paper: [2010.07492] NeRF++: Analyzing and Improving Neural Radiance Fields (arxiv.org) NeRF++: Analyzing and Improving Neural Radiance Fields Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings, including 360 capture of bounded scenes a.. [CLIP 논문 리뷰] - Learning Transferable Visual Models From Natural Language Supervision *CLIP 논문 리뷰를 위한 글입니다. 질문이 있다면 댓글로 남겨주시길 바랍니다! CLIP paper: [2103.00020] Learning Transferable Visual Models From Natural Language Supervision (arxiv.org) Learning Transferable Visual Models From Natural Language Supervision State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and.. [Barlow Twins 논문 리뷰] - Barlow Twins: Self-Supervised Learning via Redundancy Reduction *Barlow Twins 논문 리뷰를 코드와 같이 분석한 글입니다! SSL 입문하시는 분들께 도움이 되길 원하며 궁금한 점은 댓글로 남겨주세요. Barlow Twins paper: https://arxiv.org/abs/2103.03230 Barlow Twins: Self-Supervised Learning via Redundancy Reduction Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are invariant to distor.. [Simsiam 논문 리뷰] - Exploring Simple Siamese Representation Learning *Simsiam 논문 리뷰를 코드와 같이 분석한 글입니다! SSL 입문하시는 분들께 도움이 되길 원하며 궁금한 점은 댓글로 남겨주세요. *Simsiam는 Non-contrastive learning입니다. Simsiam paper: https://arxiv.org/abs/2011.10566 Exploring Simple Siamese Representation Learning Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations o.. [BYOL 논문 리뷰] - Bootstrap your own latent: A new approach to self-supervised Learning *BYOL 논문 리뷰를 코드와 같이 분석한 글입니다! SSL 입문하시는 분들께 도움이 되길 원하며 궁금한 점은 댓글로 남겨주세요. *BYOL는 Non-contrastive learning입니다. BYOL paper: https://arxiv.org/abs/2006.07733 Bootstrap your own latent: A new approach to self-supervised Learning We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and .. [MoCo 논문 리뷰] - Momentum Contrast for Unsupervised Visual Representation Learning *MoCo 논문 리뷰를 코드와 같이 분석한 글입니다! SSL 입문하시는 분들께 도움이 되길 원하며 궁금한 점은 댓글로 남겨주세요. *SSL(Self-Supervised-Learning) 중 contrastive learning을 위주로 다룹니다! Moco paper: [1911.05722] Momentum Contrast for Unsupervised Visual Representation Learning (arxiv.org) Momentum Contrast for Unsupervised Visual Representation Learning We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a.. [SimCLR 논문 리뷰] - A Simple Framework for Contrastive Learning of Visual Representations *SimCLR 논문 리뷰를 위한 글입니다! SSL 입문하시는 분들께 도움이 되길 원하며 궁금한 점은 댓글로 남겨주세요. *SSL(Self-Supervised-Learning) 중 contrastive learning을 위주로 다룹니다! *해당 글에서는 Proxy task 논문, Exemplar, Jigsaw Puzzle에 대한 간단한 설명도 포함되어 있습니다. SimCLR paper: [2002.05709] A Simple Framework for Contrastive Learning of Visual Representations (arxiv.org) A Simple Framework for Contrastive Learning of Visual Representations This paper prese.. [FissureNet 논문 리뷰] - FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images * 해당 글은 논문 리뷰를 위한 글이고, 궁금하신 점이 있다면 댓글로 남겨주세요! FissureNet paper: FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images - PMC (nih.gov) FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images Pulmonary fissure detection in computed tomography (CT) is a critical component for automatic lobar segmentation. The majority of fissure detection method.. 이전 1 ··· 5 6 7 8 9 다음
