Object Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 18
- 보존된 평가 기록
- 51
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/ErikGDev/instance-segmentation
https://github.com/FelixMQuintana/Deep-Vasp-E
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/FlowWind1999/pointnet-2
https://github.com/FrancescoSaverioZuppichini/A-journey-into-Convolutional-Neural-Network-visualization-
https://github.com/GAOwy123/py-faster-rcnn
https://github.com/GH3927/Mask-RCNN-applied-to-cranes
https://github.com/Gaurav14cs17/YOLOE
Learning Open-World Object Proposals without Learning to Classify
https://github.com/GenDisc/IVR
Evaluating Weakly Supervised Object Localization Methods Right
https://github.com/George-Ogden/Mask-RCNN
https://github.com/GoAhead106283/MURA_deep_learning
https://github.com/HRanWang/SA
https://github.com/HRanWang/Spatial-Attention
https://github.com/HRanWang/Spatial-Re-Scaling
https://github.com/HaipengXiong/weighted-hausdorff-loss
https://github.com/Harut0726/my-pointnet-tensorflow
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/Hayashi-Yudai/ML_models
Adversarial Complementary Learning for Weakly Supervised Object Localization
https://github.com/Heedeok/frustum-pointnets-rgbd
https://github.com/HichemDebbi/CexCNN
https://github.com/HollrayChan/gradcam-plus-plus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/HuynhThanhQuan/skin-detective
https://github.com/IBM/MAX-Object-Detector
https://github.com/IBM/UQ360
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors
https://github.com/IE-group/conference_report
Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
https://github.com/IISCAditayTripathi/SketchGuidedLocalization
https://github.com/IlyaDobrynin/GridMixup
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/Instinct323/wiou/blob/v2/iou.py
Focal and Efficient IOU Loss for Accurate Bounding Box Regression
https://github.com/IuliiaZaitova/code_slingers
https://github.com/Jeff-HOU/ML_Note
https://github.com/JepsonWong/CNN_Visualization
https://github.com/KMnP/fashionpedia-api
https://github.com/KaidongLi/tf-3d-alpha
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/KangBK0120/CAM
https://github.com/Kaushal28/CutMix-Regularization-using-PyTorch
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/KhusDM/PointNetTree
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/KiranAkadas/My_Pointnet_v2
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.