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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 2130 of 53 papers

TitleStatusHype
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection0
Scaling Novel Object Detection with Weakly Supervised Detection TransformersCode1
LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving0
Visual Understanding of Complex Table Structures from Document Images0
CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox DetectorCode0
Partially-Supervised Novel Object Captioning Leveraging Context from Paired Data0
A Unified Objective for Novel Class DiscoveryCode1
SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite ImageryCode1
Instance Segmentation of Microscopic ForaminiferaCode0
Oriented Bounding Boxes for Small and Freely Rotated Objects0
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