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/KostadinovShalon/oln-mask
Learning Open-World Object Proposals without Learning to Classify
https://github.com/Krupal09/MaskRCNN-Demo
https://github.com/LONG-9621/Extract_Point_3D
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/LONG-9621/PointNet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/LONG-9621/PointNet-
https://github.com/LebronGG/PointNet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/LinZhuoChen/pointnet2_multi_gpu
https://github.com/Lopezurrutia/DSB_2018
https://github.com/Lw510107/PointNet
https://github.com/Lw510107/pointnet-2018.6.27-
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/MIC-DKFZ/DetectionAndRegression
https://github.com/MIC-DKFZ/RegRCNN
https://github.com/MIT-SPARK/Kimera-VIO
Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs
https://github.com/MS-Mind/MS-Code-06/tree/main/pointpillars
https://github.com/Makunda/DeepLearningASL
https://github.com/ManooshSamiei/Object-Detection-Deep-Reinforcement-Learning
https://github.com/MarcBS/keras
https://github.com/MartinBuessemeyer/Artistic-Texture-Control
https://github.com/MikeS96/3d_obj_detection
https://github.com/MinSongKi/InCA.github.io
In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object Localization
https://github.com/Mind23-2/MindCode-169
https://github.com/Mind23-2/MindCode-173
https://github.com/MindSpore-paper-code-2/code3/tree/main/tinydarknet
https://github.com/MindSpore-paper-code-2/code399/tree/main/Transformer
A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
https://github.com/MindSpore-paper-code-3/code10/tree/main/frustum-pointnet
https://github.com/MindSpore-paper-code-3/code3/tree/main/faster_rcnn_dcn
https://github.com/MindSpore-paper-code-3/code4/tree/main/HDR-Transformer
A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
https://github.com/MindSpore-paper-code-3/code7/tree/main/frustum-pointnet
https://github.com/MindSpore-paper-code-3/code9/tree/main/MaskRCNN
https://github.com/ModelBunker/PointNet-TensorFlow
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/MoonBlvd/tad-IROS2019-TBD-
Unsupervised Traffic Accident Detection in First-Person Videos
https://github.com/Mrnoorsingh/car-parking
https://github.com/N0vel/weighted-hausdorff-distance-tensorflow-keras-loss
https://github.com/NVlabs/industreallib
https://github.com/Nacriema/Loss-Functions-For-Semantic-Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.