Object Localization

159 papers with code • 17 benchmarks • 14 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

Libraries

Use these libraries to find Object Localization models and implementations

Most implemented papers

Mask R-CNN

matterport/Mask_RCNN 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 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 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.

Learning Deep Features for Discriminative Localization

zhoubolei/CAM CVPR 2016

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 ICCV 2019

Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers.

Microsoft COCO: Common Objects in Context

PaddlePaddle/PaddleDetection 1 May 2014

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.

Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

adityac94/Grad_CAM_plus_plus 30 Oct 2017

Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.

Locating Objects Without Bounding Boxes

javiribera/locating-objects-without-bboxes CVPR 2019

In these networks, the training procedure usually requires providing bounding boxes or the maximum number of expected objects.

Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World

vict0rsch/PaperMemory 20 Mar 2017

Bridging the 'reality gap' that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability.

The Earth ain't Flat: Monocular Reconstruction of Vehicles on Steep and Graded Roads from a Moving Camera

sarthaksharma13/IROS18 6 Mar 2018

The proposed approach significantly improves the state-of-the-art for monocular object localization on arbitrarily-shaped roads.