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Open World Object Detection

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보존된 평가 기록: 12

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.

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Decoupled PROB: Decoupled Query Initialization Tasks and Objectness-Class Learning for Open World Object Detection

SAM2Auto: Auto Annotation Using FLASH

VL-SAM-V2: Open-World Object Detection with General and Specific Query Fusion

YOLO-UniOW: Efficient Universal Open-World Object Detection

UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set Object Detection Framework

From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects

OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection

DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding

Open World Object Detection: A Survey

SIA-OVD: Shape-Invariant Adapter for Bridging the Image-Region Gap in Open-Vocabulary Detection

OW-Rep: Open World Object Detection with Instance Representation Learning

Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes

Detecting Every Object from Events

YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery

VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation

BSDP: Brain-inspired Streaming Dual-level Perturbations for Online Open World Object Detection

Semi-supervised Open-World Object Detection

SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object Detector

Open World Object Detection in the Era of Foundation Models

Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased Detector

Recognize Any Regions

Unsupervised Recognition of Unknown Objects for Open-World Object Detection

Random Boxes Are Open-world Object Detectors

Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection

USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model

Addressing the Challenges of Open-World Object Detection

Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects

Detecting the open-world objects with the help of the Brain

Detecting Everything in the Open World: Towards Universal Object Detection

Open-World Object Detection via Discriminative Class Prototype Learning

CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection

GOOD: Exploring Geometric Cues for Detecting Objects in an Open World

Open World DETR: Transformer based Open World Object Detection

PROB: Probabilistic Objectness for Open World Object Detection

DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection

UC-OWOD: Unknown-Classified Open World Object Detection

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