Object Localization

118 papers with code • 15 benchmarks • 10 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

Greatest papers with code

Grid R-CNN

open-mmlab/mmdetection CVPR 2019

This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection.

Object Detection Object Localization

Learning Deep Features for Discriminative Localization

tensorpack/tensorpack 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.

Weakly-Supervised Object Localization

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.

Instance Segmentation Object Localization +3

Eigen-CAM: Class Activation Map using Principal Components

jacobgil/pytorch-grad-cam 1 Aug 2020

At the heart of this progress is convolutional neural networks (CNNs) that are capable of learning representations or features given a set of data.

Weakly-Supervised Object Localization

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

jacobgil/pytorch-grad-cam 30 Oct 2017

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

3D Action Recognition Knowledge Distillation +1

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

charlesq34/pointnet 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.

3D Object Detection Autonomous Navigation +5

Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond

PaddlePaddle/PaddleClas 6 Nov 2018

Our approach only needs to modify the input image and can work with any network to improve its performance.

Data Augmentation Emotion Recognition +5

Dilated Residual Networks

osmr/imgclsmob CVPR 2017

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.

Classification General Classification +4