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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 5153 of 53 papers

TitleStatusHype
Partially-Supervised Novel Object Captioning Leveraging Context from Paired Data0
PlantDet: A benchmark for Plant Detection in the Three-Rivers-Source Region0
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection0
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