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
Latest papers with no code
Improving Weakly-Supervised Object Localization Using Adversarial Erasing and Pseudo Label
This paper investigates a framework for weakly-supervised object localization, which aims to train a neural network capable of predicting both the object class and its location using only images and their image-level class labels.
Real-world Instance-specific Image Goal Navigation for Service Robots: Bridging the Domain Gap with Contrastive Learning
To address this, we propose a novel method called Few-shot Cross-quality Instance-aware Adaptation (CrossIA), which employs contrastive learning with an instance classifier to align features between massive low- and few high-quality images.
IDD-X: A Multi-View Dataset for Ego-relative Important Object Localization and Explanation in Dense and Unstructured Traffic
Intelligent vehicle systems require a deep understanding of the interplay between road conditions, surrounding entities, and the ego vehicle's driving behavior for safe and efficient navigation.
O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation
Online construction of open-ended language scenes is crucial for robotic applications, where open-vocabulary interactive scene understanding is required.
MOSE: Boosting Vision-based Roadside 3D Object Detection with Scene Cues
3D object detection based on roadside cameras is an additional way for autonomous driving to alleviate the challenges of occlusion and short perception range from vehicle cameras.
Towards Two-Stream Foveation-based Active Vision Learning
Specifically, the proposed framework models the following mechanisms: 1) ventral (what) stream focusing on the input regions perceived by the fovea part of an eye (foveation), 2) dorsal (where) stream providing visual guidance, and 3) iterative processing of the two streams to calibrate visual focus and process the sequence of focused image patches.
Spatio-Temporal Bi-directional Cross-frame Memory for Distractor Filtering Point Cloud Single Object Tracking
This integrates future and synthetic past frame memory to enhance the current memory, thereby improving the accuracy of iteration-based tracking.
Point-DETR3D: Leveraging Imagery Data with Spatial Point Prior for Weakly Semi-supervised 3D Object Detection
Training high-accuracy 3D detectors necessitates massive labeled 3D annotations with 7 degree-of-freedom, which is laborious and time-consuming.
EcoSense: Energy-Efficient Intelligent Sensing for In-Shore Ship Detection through Edge-Cloud Collaboration
Detecting marine objects inshore presents challenges owing to algorithmic intricacies and complexities in system deployment.
Could We Generate Cytology Images from Histopathology Images? An Empirical Study
Automation in medical imaging is quite challenging due to the unavailability of annotated datasets and the scarcity of domain experts.
