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/lufan31/comprecap
https://github.com/lukemelas/deep-spectral-segmentation
Localizing Objects with Self-Supervised Transformers and no Labels
https://github.com/lukoucky/image_recommendation
https://github.com/lwhyc/oneshot_weaklyseg
https://github.com/manthanpatel2/Object-Detection-on-a-Frame
https://github.com/maxfrei750/DeepParticleNet
https://github.com/maxfrei750/FibeR-CNN
https://github.com/metalbubble/CAM
https://github.com/miaohua1982/simple_fasterrcnn_pytorch
https://github.com/mic-dkfz/medicaldetectiontoolkit
https://github.com/mikuhatsune/dap
https://github.com/mindspore-ai/models/tree/master/official/cv/maskrcnn
https://github.com/minimaxir/person-blocker
https://github.com/mirzaevinom/data_science_bowl_2018
https://github.com/mrahtz/learning-from-human-preferences
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
https://github.com/mstfakts/building-detection-maskrcnn
https://github.com/multimodallearning/pytorch-mask-rcnn
https://github.com/muyistarsky/MaskRCNN
https://github.com/mveres01/grasping
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
https://github.com/nalbert9/Image-Captioning
https://github.com/naver-ai/coco-annotation-tool
https://github.com/neka-nat/gazebo_domain_randomization
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
https://github.com/nightrome/cocostuff10k
https://github.com/nikhithakarennagari/1311
https://github.com/nikhithakarennagari/Git
https://github.com/noahj08/DeepConnotation
https://github.com/noelcodes/Mask_RCNN
https://github.com/o-evgeny/MRCNN_DeepFashion2
https://github.com/open-edge-platform/geti
https://github.com/open-edge-platform/training_extensions
https://github.com/open-mmlab/mmclassification
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/open-mmlab/mmdetection
https://github.com/open-mmlab/mmpose
https://github.com/open-mmlab/mmtracking
DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion
https://github.com/osmr/imgclsmob
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.