Object Localization
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Object Localization is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. An object proposal specifies a candidate bounding box, and an object proposal is said to be a correct localization if it sufficiently overlaps a human-labeled “ground-truth” bounding box for the given object. In the literature, the “Object Localization” task is to locate one instance of an object category, whereas “object detection” focuses on locating all instances of a category in a given image.
Source: Fast On-Line Kernel Density Estimation for Active Object Localization
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Okery/PyTorch-Simple-MaskRCNN
https://github.com/PaddlePaddle/PaddleClas
Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PsorTheDoctor/microarray-data
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/Qinhj07/ATOMCode
https://github.com/RPFey/frustum-pointnets
https://github.com/RaiyaniNirav/Mask-R-CNN-for-water-detection
https://github.com/RajArPatra/Super-OCR
https://github.com/Rakeshvd/Instance-Segmenation-of-MRI-scan-using-Mask-RCNN
https://github.com/Rakeshvd/Semantic-Segmenation-of-MRI-scan-using-Mask-RCNN
https://github.com/RishiTejaMadduri/VoxelNet_PyTorch
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/RituYadav92/Radar-RGB-Attentive-Multimodal-Object-Detection
https://github.com/RylanSchaeffer/prismatic-vlms
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
https://github.com/SEU-SRTP-GROUP/frustum-pointnet-original
https://github.com/SSN15/Behavioral-Cloning--Implementaion-of-Autonomous-car-using-deep-learning
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/STVIR/Grid-R-CNN
https://github.com/SUYEgit/Surgery-Robot-Detection-Segmentation
https://github.com/Seb-Good/deep_ecg
https://github.com/Seb-Good/deepecg
https://github.com/SfTI-Robotics/ROS-label-node
https://github.com/Smiler-Jin/frustum_pointnet
https://github.com/SonginCV/GMPHD_MAF
https://github.com/SonginCV/GMPHD_SAF
https://github.com/SonginCV/MAF_HDA
https://github.com/StephenEkaputra/Mask_RCNN-TinyPascalVOC
https://github.com/SyogoShibuya/Chainer-Cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/TUMFTM/RadarVoxelFusionNet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/TejasBajania/Mtech_pro
https://github.com/TejasBajania/Mtech_thesis_project
https://github.com/TianshuXie/Cut-Thumbnail
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/TimurIbrayev/FALcon
https://github.com/TooTouch/WhiteBox-Part1
https://github.com/TuSimple/mx-maskrcnn
https://github.com/UPCLJ/py-faster-rcnn
https://github.com/VaanHUANG/CSCI5210HW1
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.