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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
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from VideosCode0
Small Instance Detection by Integer Programming on Object Density Maps0
Accurate Object Detection with Joint Classification-Regression Random Forests0
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