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/toshi-k/kaggle-bengaliai-handwritten-grapheme-classification
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/totti0223/gradcamplusplus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/tristangomez44/metrics-saliency-maps
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/tryolabs/luminoth
https://github.com/ucas-vg/TinyBenchmark
https://github.com/unveilx/slm-od-ml-comparison
Evaluation and Comparison of Visual Language Models for Transportation Engineering Problems
https://github.com/v1an1/gradcamplusplus-cats-v-dogs
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/vadimkantorov/contextlocnet
ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
https://github.com/vasgaowei/ts-cam-voc
TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization
https://github.com/vcowwy/paddle-grad-cam
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/vict0rsch/PaperMemory
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
https://github.com/vimalabs/VIMA
https://github.com/vios-s/multiscale-adversarial-attention-gates
Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
https://github.com/vlcekl/n2n-tomo
https://github.com/vlue-c/PyTorch-Explanations
https://github.com/vlue-c/Visual-Explanation-Methods-PyTorch
https://github.com/vnbot2/object-locator
https://github.com/voidrank/Geo-CNN
https://github.com/wangermeng2021/FastClassification
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/wangxiao5791509/Tracking-with-Deep-Reinforcement-Learning
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search
https://github.com/waspinator/deep-learning-explorer
https://github.com/wonderland-dsg/pointnet-grid
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/wufan-tb/gmm_dae
Video Anomaly Detection by Estimating Likelihood of Representations
https://github.com/wuryantoAji/POINTNET
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/xden2331/attentive_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/xiaoling2yy/Bounding-Box-Regression-with-Uncertainty-for-Accurate-Object-Detection
Bounding Box Regression with Uncertainty for Accurate Object Detection
https://github.com/xinjinghao/sparrow-v1
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
https://github.com/xinyangj/YOLaT-VectorGraphicsRecognition-1
https://github.com/xiuyu0000/papers_with_examples/tree/main/maskrcnn
https://github.com/xjnpark/ds
https://github.com/xunhen/py-faster-rcnn-wjc
https://github.com/xurui1217/pointnet2-master
https://github.com/xzabg/faster-rcnn-with-Caltech
https://github.com/y2kmz/pointnetv2
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/yangcaoai/CoDA_NeurIPS2023
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.