Object Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/ben0110/Radar-Pointnet-PARA
https://github.com/ben0110/frustum-pointnets_RSC_RADAR_fil_PC_batch_para
https://github.com/bethgelab/siamese-mask-rcnn
https://github.com/boom85423/Cartoon-style-Stickers-Generator
https://github.com/bowu1004/instance_segmentation_RealSense
https://github.com/brbzjl/pointnet2
https://github.com/bt77/pointnet
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/busyboxs/What-I-have-star
https://github.com/busyboxs/faster_rcnn_voc
https://github.com/casiopa/Madrid_Rooftops
https://github.com/cauchyturing/UCR_Time_Series_Classification_Deep_Learning_Baseline
https://github.com/chandrakantkhandelwal/PracticeCodes
https://github.com/charlesYangM/py-faster-rcnn-80.28
https://github.com/charlesq34/pointnet
https://github.com/charlesq34/pointnet2
https://github.com/charlesshang/fastmaskrcnn
https://github.com/chenwuperth/ClaRAN
https://github.com/chihyanghsu0805/object_detection_yolo
https://github.com/chodi150/scanrefer-pointgroup
ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language
https://github.com/chonepieceyb/reading-frustum-pointnets-code
https://github.com/chuanqichen/deepcoaching
https://github.com/chwilms/sos
Learning Open-World Object Proposals without Learning to Classify
https://github.com/cj-mclaughlin/segmentation_research
https://github.com/cmu-enyac/Renofeation
https://github.com/coconutzs/PointNet_zs
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/code-implementation1/Code9/tree/main/MaskRCNN/maskrcnn_mobilenetv1
https://github.com/code-implementation1/Code9/tree/main/PointNet2
https://github.com/cokowpublic/Nuclei-Segmentation
https://github.com/collectionslab/Omniscribe
https://github.com/collectionslab/annotations-computervision
https://github.com/collectionslab/book-annotation-classification
https://github.com/cpuimage/DualAttentionGuidedDropout
Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization
https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn
https://github.com/cvi-szu/clims
https://github.com/cwbabel/faster-cnn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.