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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 1–50 of 50 papers

TitleStatusHype
DINO-X: A Unified Vision Model for Open-World Object Detection and UnderstandingCode5
Exploring Orthogonality in Open World Object DetectionCode2
YOLO-UniOW: Efficient Universal Open-World Object DetectionCode2
Detecting Everything in the Open World: Towards Universal Object DetectionCode2
Open World Object Detection: A SurveyCode2
OpenAD: Open-World Autonomous Driving Benchmark for 3D Object DetectionCode2
Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown ObjectsCode1
Annealing-Based Label-Transfer Learning for Open World Object DetectionCode1
SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object DetectorCode1
Detecting Every Object from EventsCode1
Detecting the open-world objects with the help of the BrainCode1
From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel ObjectsCode1
GOOD: Exploring Geometric Cues for Detecting Objects in an Open WorldCode1
Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object DetectionCode1
Learning Open-World Object Proposals without Learning to ClassifyCode1
Localized Vision-Language Matching for Open-vocabulary Object DetectionCode1
Class-agnostic Object Detection with Multi-modal TransformerCode1
OW-DETR: Open-world Detection TransformerCode1
OW-OVD: Unified Open World and Open Vocabulary Object DetectionCode1
PROB: Probabilistic Objectness for Open World Object DetectionCode1
Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased DetectorCode1
Random Boxes Are Open-world Object DetectorsCode1
Recognize Any RegionsCode1
Revisiting Open World Object DetectionCode1
Semi-supervised Open-World Object DetectionCode1
SIA-OVD: Shape-Invariant Adapter for Bridging the Image-Region Gap in Open-Vocabulary DetectionCode1
Towards Open World Object DetectionCode1
UC-OWOD: Unknown-Classified Open World Object DetectionCode1
Unsupervised Recognition of Unknown Objects for Open-World Object DetectionCode1
VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual NavigationCode1
Detecting Open World Objects via Partial Attribute Assignment—0
SAM2Auto: Auto Annotation Using FLASH—0
DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection—0
Decoupled PROB: Decoupled Query Initialization Tasks and Objectness-Class Learning for Open World Object Detection—0
Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes—0
YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery—0
USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model—0
BSDP: Brain-inspired Streaming Dual-level Perturbations for Online Open World Object Detection—0
UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set Object Detection Framework—0
VL-SAM-V2: Open-World Object Detection with General and Specific Query Fusion—0
Rectifying Open-set Object Detection: A Taxonomy, Practical Applications, and Proper Evaluation—0
Addressing the Challenges of Open-World Object Detection—0
Objects in Semantic Topology—0
Contrastive Object Detection Using Knowledge Graph Embeddings—0
Open World DETR: Transformer based Open World Object Detection—0
CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection—0
Open World Object Detection in the Era of Foundation Models—0
Open-World Object Detection via Discriminative Class Prototype Learning—0
OW-Rep: Open World Object Detection with Instance Representation Learning—0
Open-World Objectness Modeling Unifies Novel Object Detection—0
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