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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 26–50 of 50 papers

TitleStatusHype
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
USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model—0
Addressing the Challenges of Open-World Object Detection—0
Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown ObjectsCode1
Detecting Everything in the Open World: Towards Universal Object DetectionCode2
Detecting the open-world objects with the help of the BrainCode1
Open-World Object Detection via Discriminative Class Prototype Learning—0
CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection—0
Annealing-Based Label-Transfer Learning for Open World Object DetectionCode1
GOOD: Exploring Geometric Cues for Detecting Objects in an Open WorldCode1
Open World DETR: Transformer based Open World Object Detection—0
PROB: Probabilistic Objectness for Open World Object DetectionCode1
DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection—0
UC-OWOD: Unknown-Classified Open World Object DetectionCode1
Rectifying Open-set Object Detection: A Taxonomy, Practical Applications, and Proper Evaluation—0
Localized Vision-Language Matching for Open-vocabulary Object DetectionCode1
Revisiting Open World Object DetectionCode1
Contrastive Object Detection Using Knowledge Graph Embeddings—0
OW-DETR: Open-world Detection TransformerCode1
Class-agnostic Object Detection with Multi-modal TransformerCode1
Objects in Semantic Topology—0
Learning Open-World Object Proposals without Learning to ClassifyCode1
Towards Open World Object DetectionCode1
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