SOTAVerified

Real-Time Object Detection

Real-Time Object Detection is a computer vision task that involves identifying and locating objects of interest in real-time video sequences with fast inference while maintaining a base level of accuracy.

This is typically solved using algorithms that combine object detection and tracking techniques to accurately detect and track objects in real-time. They use a combination of feature extraction, object proposal generation, and classification to detect and localize objects of interest.

( Image credit: CenterNet )

Papers

Showing 5160 of 259 papers

TitleStatusHype
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksCode1
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object DetectionCode1
Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep LearningCode1
RegionCLIP: Region-based Language-Image PretrainingCode1
Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion DetectionCode1
CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing ImagesCode1
CenterMask : Real-Time Anchor-Free Instance SegmentationCode1
CBNet: A Composite Backbone Network Architecture for Object DetectionCode1
End-to-End Object Detection with TransformersCode1
RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor DetectionCode1
Show:102550
← PrevPage 6 of 26Next →

No leaderboard results yet.