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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

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Showing 21–30 of 53 papers

TitleStatusHype
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from VideosCode0
Learning to Detect and Retrieve Objects from Unlabeled VideosCode0
Automatic Signboard Detection and Localization in Densely Populated Developing CitiesCode0
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing ImageryCode0
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection—0
Small Instance Detection by Integer Programming on Object Density Maps—0
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision—0
Visual Understanding of Complex Table Structures from Document Images—0
Accurate Object Detection with Joint Classification-Regression Random Forests—0
WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images—0
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