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 41–50 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 Features—0
Grid R-CNNCode0
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection—0
Object Detection based Deep Unsupervised Hashing—0
Deep Watershed Detector for Music Object Recognition—0
Deep Regionlets for Object Detection—0
CAD Priors for Accurate and Flexible Instance Reconstruction—0
Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop ObjectsCode0
Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection—0
Show:102550
← PrevPage 5 of 6Next →

No leaderboard results yet.