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
Bounding Box Regression with Uncertainty for Accurate Object Detection
yihui-he/KL-Loss
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CVPR 2019
Large-scale object detection datasets (e. g., MS-COCO) try to define the ground truth bounding boxes as clear as possible.
Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models
frgfm/torch-cam
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With the intention to create an enhanced visual explanation in terms of visual sharpness, object localization and explaining multiple occurrences of objects in a single image, we present Smooth Grad-CAM++ \footnote{Simple demo: http://35. 238. 22. 135:5000/}, a technique that combines methods from two other recent techniques---SMOOTHGRAD and Grad-CAM++.
BOP Challenge 2020 on 6D Object Localization
This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D image.
Active Object Localization with Deep Reinforcement Learning
otoofim/ObjLocalisation
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ICCV 2015
We present an active detection model for localizing objects in scenes.
Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization
We propose `Hide-and-Seek', a weakly-supervised framework that aims to improve object localization in images and action localization in videos.
Dilated Residual Networks
Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible.
Unsupervised Traffic Accident Detection in First-Person Videos
Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems.
ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language
daveredrum/ScanRefer
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ECCV 2020
We introduce the task of 3D object localization in RGB-D scans using natural language descriptions.
Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
gvalvano/multiscale-adversarial-attention-gates
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We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks.
Eigen-CAM: Class Activation Map using Principal Components
At the heart of this progress is convolutional neural networks (CNNs) that are capable of learning representations or features given a set of data.
