SOTAVerified

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 11–20 of 50 papers

TitleStatusHype
Detecting the open-world objects with the help of the BrainCode1
Detecting Every Object from EventsCode1
GOOD: Exploring Geometric Cues for Detecting Objects in an Open WorldCode1
OW-OVD: Unified Open World and Open Vocabulary Object DetectionCode1
From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel ObjectsCode1
Annealing-Based Label-Transfer Learning for Open World Object DetectionCode1
Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object DetectionCode1
Learning Open-World Object Proposals without Learning to ClassifyCode1
Class-agnostic Object Detection with Multi-modal TransformerCode1
OW-DETR: Open-world Detection TransformerCode1
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