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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
DINO-X: A Unified Vision Model for Open-World Object Detection and UnderstandingCode5
Open World Object Detection: A SurveyCode2
SIA-OVD: Shape-Invariant Adapter for Bridging the Image-Region Gap in Open-Vocabulary DetectionCode1
OW-Rep: Open World Object Detection with Instance Representation Learning—0
Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes—0
Detecting Every Object from EventsCode1
YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery—0
VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual NavigationCode1
BSDP: Brain-inspired Streaming Dual-level Perturbations for Online Open World Object Detection—0
Semi-supervised Open-World Object DetectionCode1
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