Object Localization
232 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
Latest papers with no code
BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection
Recently, the rise of query-based Transformer decoders is reshaping camera-based 3D object detection.
SANeRF-HQ: Segment Anything for NeRF in High Quality
Recently, the Segment Anything Model (SAM) has showcased remarkable capabilities of zero-shot segmentation, while NeRF (Neural Radiance Fields) has gained popularity as a method for various 3D problems beyond novel view synthesis.
Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding
In this work, we introduce Language Embedded 3D Gaussians, a novel scene representation for open-vocabulary query tasks.
Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI
The clinical management of breast cancer depends on an accurate understanding of the tumor and its anatomical context to adjacent tissues and landmark structures.
Cooperative Multi-Monostatic Sensing for Object Localization in 6G Networks
Enabling passive sensing of the environment using cellular base stations (BSs) will be one of the disruptive features of the sixth-generation (6G) networks.
DUA-DA: Distillation-based Unbiased Alignment for Domain Adaptive Object Detection
Though feature-alignment based Domain Adaptive Object Detection (DAOD) have achieved remarkable progress, they ignore the source bias issue, i. e. the aligned features are more favorable towards the source domain, leading to a sub-optimal adaptation.
Object Pose Estimation Annotation Pipeline for Multi-view Monocular Camera Systems in Industrial Settings
A more practical approach is to utilize existing cameras in such spaces in order to address the underlying pose estimation problem and to localize objects of interest.
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement
While CLIP is scalable, promptable, and robust to distribution shifts on image classification tasks, it lacks object localization capabilities.
Memory-efficient particle filter recurrent neural network for object localization
This study proposes a novel memory-efficient recurrent neural network (RNN) architecture specified to solve the object localization problem.
DeepAdaIn-Net: Deep Adaptive Device-Edge Collaborative Inference for Augmented Reality
Specifically, DeepAdaIn-Net encompasses a partition point selection (PPS) module, a high feature compression learning (HFCL) module, a bandwidth-aware feature configuration (BaFC) module, and a feature consistency compensation (FCC) module.
