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Open World Object Detection

Open World Object Detection is a computer vision problem where a model is tasked to: 1) identify objects that have not been introduced to it as `unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received.

Papers

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Showing 41–50 of 50 papers

TitleStatusHype
Rectifying Open-set Object Detection: A Taxonomy, Practical Applications, and Proper Evaluation—0
Addressing the Challenges of Open-World Object Detection—0
Objects in Semantic Topology—0
Contrastive Object Detection Using Knowledge Graph Embeddings—0
Open World DETR: Transformer based Open World Object Detection—0
CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection—0
Open World Object Detection in the Era of Foundation Models—0
Open-World Object Detection via Discriminative Class Prototype Learning—0
OW-Rep: Open World Object Detection with Instance Representation Learning—0
Open-World Objectness Modeling Unifies Novel Object Detection—0
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