SOTAVerified

Novel Object Detection

Novel Object Detection is a challenging task introduced by Fomenko et.al. in their paper "Learning to Discover and Detect Objects". The goal in this task is to measure mAP performance on known as well as novel classes, where the known classes correspond to the 80 COCO classes, and the novel classes are the remaining 1123 classes from LVIS dataset. Thus, during training the model can only be trained with annotations from COCO dataset, but during evaluation/inference it is expected to BOTH classify and detect objects belonging to ALL the classes in the LVIS dataset.

Papers

Showing 4150 of 53 papers

TitleStatusHype
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing ImageryCode0
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features0
Grid R-CNNCode0
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection0
Object Detection based Deep Unsupervised Hashing0
Deep Watershed Detector for Music Object Recognition0
Deep Regionlets for Object Detection0
CAD Priors for Accurate and Flexible Instance Reconstruction0
Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop ObjectsCode0
Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection0
Show:102550
← PrevPage 5 of 6Next →

No leaderboard results yet.