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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 11–20 of 50 papers

TitleStatusHype
VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual NavigationCode1
Semi-supervised Open-World Object DetectionCode1
SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object DetectorCode1
Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased DetectorCode1
Recognize Any RegionsCode1
Unsupervised Recognition of Unknown Objects for Open-World Object DetectionCode1
Random Boxes Are Open-world Object DetectorsCode1
Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object DetectionCode1
Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown ObjectsCode1
Detecting the open-world objects with the help of the BrainCode1
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