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 3140 of 53 papers

TitleStatusHype
An object detection approach for lane change and overtake detection from motion profiles0
Any-Shot Object Detection0
CAD Priors for Accurate and Flexible Instance Reconstruction0
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection0
Deep Regionlets for Object Detection0
Deep Watershed Detector for Music Object Recognition0
Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection0
EMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection0
FA-YOLO: Research On Efficient Feature Selection YOLO Improved Algorithm Based On FMDS and AGMF Modules0
Knowledge Guided Learning: Towards Open Domain Egocentric Action Recognition with Zero Supervision0
Show:102550
← PrevPage 4 of 6Next →

No leaderboard results yet.