Object Localization
231 papers with code • 18 benchmarks • 17 datasets
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
Benchmarks
These leaderboards are used to track progress in Object Localization
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Libraries
Use these libraries to find Object Localization models and implementations
MS COCO
KITTI
PASCAL VOC 2007
Mall
HDD
ScanRefer Dataset
SIXray
UVO
GRIT
RF100
Subtasks
Most implemented papers
Mask R-CNN
tensorflow/models
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ICCV 2017
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
Frustum PointNets for 3D Object Detection from RGB-D Data
charlesq34/frustum-pointnets
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CVPR 2018
In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
qianguih/voxelnet
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CVPR 2018
Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
Microsoft COCO: Common Objects in Context
PaddlePaddle/PaddleDetection
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We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding.
Learning Deep Features for Discriminative Localization
In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels.
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
clovaai/CutMix-PyTorch
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ICCV 2019
Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers.
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
adityac94/Grad_CAM_plus_plus
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Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
Bridging the 'reality gap' that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability.
Locating Objects Without Bounding Boxes
javiribera/locating-objects-without-bboxes
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CVPR 2019
In these networks, the training procedure usually requires providing bounding boxes or the maximum number of expected objects.
The Earth ain't Flat: Monocular Reconstruction of Vehicles on Steep and Graded Roads from a Moving Camera
The proposed approach significantly improves the state-of-the-art for monocular object localization on arbitrarily-shaped roads.
