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/2023-MindSpore-1/ms-code-208
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/frustum-pointnet
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tinydarknet
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/tinydarknet
https://github.com/2023-MindSpore-4/Code10/tree/main/MaskRCNN
https://github.com/2023-MindSpore-4/Code4/tree/main/tnet_mindspore
Does Thermal Really Always Matter for RGB-T Salient Object Detection?
https://github.com/2023-MindSpore-4/Code9/tree/main/MaskRCNN
https://github.com/2023-MindSpore-4/Code9/tree/main/PointNet2
https://github.com/AKASH2907/bird-species-classification
https://github.com/AKASH2907/bird_species_classification
https://github.com/AggMan96/RK-Net
https://github.com/Alexander-Whelan/Zeus
https://github.com/AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/Arthur-Shi/py-faster-rcnn
https://github.com/AshishSingh2261/Pedestrian_Instance_Segmentation
https://github.com/AustinDoolittle/Pytorch-Gain
https://github.com/Azure/AzureChestXRay
https://github.com/BPMJG/annotated-F-pointnet
https://github.com/BPMJG/annotated_pointnet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/Bennie-Han/Image-augementation-pytorch
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/Biantian/MscProject
https://github.com/BingkAI-B21CAP0161/C-Mask-Machine-Learning
https://github.com/BlackAngel1111/Fast-RCNN
https://github.com/BupyeongHealer/Mask_RCNN_tf_2.x
https://github.com/Burf/tfdetection
https://github.com/CarloSgaravatti/HybridLateCascadeFusion
https://github.com/CarstenIsert/DeepBurn
https://github.com/ColdNoodler/py-faster-rcnn-cuda10
https://github.com/CuberrChen/VoxelNet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/DLR-RM/BlenderProc
https://github.com/DeNA/Chainer_Mask_R-CNN
https://github.com/DevBruce/CutMixImageDataGenerator_For_Keras
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/DivJAth/DeepLearning5922
https://github.com/EMalagoli92/OD-Metrics
https://github.com/Ecgbert/Grad_CAM_PLUS_PLUS
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.