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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 3140 of 53 papers

TitleStatusHype
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision0
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection0
Universal-Prototype Enhancing for Few-Shot Object DetectionCode1
Open-World Semi-Supervised LearningCode1
Knowledge Guided Learning: Towards Open Domain Egocentric Action Recognition with Zero Supervision0
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAsCode1
Any-Shot Object Detection0
Automatic Signboard Detection and Localization in Densely Populated Developing CitiesCode0
Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations0
Learning to Detect and Retrieve Objects from Unlabeled VideosCode0
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