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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 41–50 of 53 papers

TitleStatusHype
Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations—0
Object Detection based Deep Unsupervised Hashing—0
Open-World Objectness Modeling Unifies Novel Object Detection—0
Oriented Bounding Boxes for Small and Freely Rotated Objects—0
Instance Segmentation of Microscopic ForaminiferaCode0
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from VideosCode0
CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox DetectorCode0
Grid R-CNNCode0
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing ImageryCode0
Beyond the Benchmark: Detecting Diverse Anomalies in VideosCode0
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