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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
Learning to Discover and Detect ObjectsCode1
DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object DetectionCode1
A Unified Objective for Novel Class DiscoveryCode1
DesCo: Learning Object Recognition with Rich Language DescriptionsCode1
Scaling Novel Object Detection with Weakly Supervised Detection TransformersCode1
Deep Watershed Detector for Music Object Recognition0
Deep Regionlets for Object Detection0
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection0
Any-Shot Object Detection0
Knowledge Guided Learning: Towards Open Domain Egocentric Action Recognition with Zero Supervision0
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