https://github.com/juergenlandauer/Maya-Challenge
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
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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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Adversarial Complementary Learning for Weakly Supervised Object Localization
Attention-based Dropout Layer for Weakly Supervised Object Localization
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Learning Open-World Object Proposals without Learning to Classify
Cross-Modal Weighting Network for RGB-D Salient Object Detection
University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
StarNet: towards Weakly Supervised Few-Shot Object Detection
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.