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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 1120 of 53 papers

TitleStatusHype
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event CameraCode1
Learning to Discover and Detect ObjectsCode1
A Unified Objective for Novel Class DiscoveryCode1
SaRNet: A Dataset for Deep Learning Assisted Search and Rescue with Satellite ImageryCode1
Enhancing Novel Object Detection via Cooperative Foundational ModelsCode1
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing ImageryCode0
Beyond the Benchmark: Detecting Diverse Anomalies in VideosCode0
CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox DetectorCode0
Automatic Signboard Detection and Localization in Densely Populated Developing CitiesCode0
Instance Segmentation of Microscopic ForaminiferaCode0
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