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/dannofield/CarND-Extended-Kalman-Filter
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/daveredrum/ScanRefer
ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language
https://github.com/delldu/MaskRCNN
https://github.com/deolipankaj/Stone_Detection_MRCNN
https://github.com/devsoft123/fast-cnn
https://github.com/diksha0799/Image-Captioning-master
https://github.com/djib2011/high-res-mapping
https://github.com/dongdong69/MixAugmentation
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/dvl-tum/motsynth-baselines
https://github.com/dwtstore/sfm1
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/eduardohenriquearnold/coop-3dod-infra
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/eknim/gradcamplusplus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/elonashatri/pitch_mask_rcnn
https://github.com/evaristr/py-faster_rcnn
https://github.com/facebookresearch/ActivityNet-Entities
Team RUC_AIM3 Technical Report at ActivityNet 2021: Entities Object Localization
https://github.com/facebookresearch/dcd
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
https://github.com/facebookresearch/detectron
https://github.com/facebookresearch/detectron2
https://github.com/facebookresearch/locate-3d
Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D
https://github.com/facebookresearch/maskrcnn-benchmark
https://github.com/facebookresearch/minimax
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
https://github.com/fatLime/Predict-Lung-Disease
https://github.com/fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/fdac18/ForensicImages
https://github.com/fiveai/saod
Bounding Box Regression with Uncertainty for Accurate Object Detection
https://github.com/frgfm/torch-cam
https://github.com/froggydisk/CAM
https://github.com/fsafe/Capstone
https://github.com/ftorres11/saliencysense
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/gfoxx29/Image-Captioning
https://github.com/goddoe/hide-and-seek
https://github.com/godspeedcurry/lung-nodule-detection
https://github.com/goodproj13/DenserNet
Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization
https://github.com/guanfuchen/py-faster-rcnn
https://github.com/gvalvano/wss-multiscale-adversarial-attention-gates
Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.