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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 1–10 of 50 papers

TitleStatusHype
Decoupled PROB: Decoupled Query Initialization Tasks and Objectness-Class Learning for Open World Object Detection—0
SAM2Auto: Auto Annotation Using FLASH—0
VL-SAM-V2: Open-World Object Detection with General and Specific Query Fusion—0
OW-OVD: Unified Open World and Open Vocabulary Object DetectionCode1
Detecting Open World Objects via Partial Attribute Assignment—0
Open-World Objectness Modeling Unifies Novel Object Detection—0
YOLO-UniOW: Efficient Universal Open-World Object DetectionCode2
UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set Object Detection Framework—0
From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel ObjectsCode1
OpenAD: Open-World Autonomous Driving Benchmark for 3D Object DetectionCode2
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