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Zero-Shot Object Detection

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Zero-shot object detection (ZSD) is the task of object detection where no visual training data is available for some of the target object classes.

<span style="color:grey; opacity: 0.6">( Image credit: Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/zhengye1995/Zero-shot-Instance-Segmentation

공식 코드

Zero-Shot Instance Segmentation

https://dagshub.com/f2010126/Zero-shot-Instance-Segmentation

Zero-Shot Instance Segmentation

https://github.com/E-DEEP/PapersReview

Zero-Shot Instance Segmentation

https://github.com/Gaurav14cs17/YOLOE

Learning Open-World Object Proposals without Learning to Classify

https://github.com/IDEA-Research/Grounded-Segment-Anything

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/KennithLi/Awesome-Zero-Shot-Object-Detection

Zero-Shot Object Detection by Hybrid Region Embedding

https://github.com/KostadinovShalon/oln-mask

Learning Open-World Object Proposals without Learning to Classify

https://github.com/MindCode-4/code-2/tree/main/owlvit

Scaling Open-Vocabulary Object Detection

https://github.com/PaddlePaddle/PaddleMIX

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/X-BrainLab/GTNet

GTNet: Generative Transfer Network for Zero-Shot Object Detection

https://github.com/anthonyhuo/sam-dam-for-compositional-reasoning

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/berkandemirel/zero-shot-detection

Zero-Shot Object Detection by Hybrid Region Embedding

https://github.com/brown-palm/ObjectPrompt

Grounded Language-Image Pre-training

https://github.com/camenduru/grounded-segment-anything-colab

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/chwilms/sos

Learning Open-World Object Proposals without Learning to Classify

https://github.com/d-robotics-ai-lab/dosod

YOLO-World: Real-Time Open-Vocabulary Object Detection

https://github.com/dyabel/detpro

Open-vocabulary Object Detection via Vision and Language Knowledge Distillation

https://github.com/eric-ai-lab/pevit

ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

https://github.com/hanoonaR/object-centric-ovd

Open-vocabulary Object Detection via Vision and Language Knowledge Distillation

https://github.com/huggingface/transformers

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/hzlbbfrog/generative-bim

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/ibaiGorordo/ONNX-Object-Localization-Network

Learning Open-World Object Proposals without Learning to Classify

https://github.com/ibaiGorordo/ONNX-YOLO-World-Open-Vocabulary-Object-Detection

YOLO-World: Real-Time Open-Vocabulary Object Detection

https://github.com/idea-research/grounded-sam-2

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/kostadinovshalon/oln-ssos

Learning Open-World Object Proposals without Learning to Classify

https://github.com/longzw1997/Open-GroundingDino

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/memoatwit/universalperturbation

Gaussian Splatting Under Attack: Investigating Adversarial Noise in 3D Objects

https://github.com/microsoft/esvit

ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

https://github.com/microsoft/klite

ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

https://github.com/microsoft/unicl

ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

https://github.com/mit-han-lab/efficientvit

Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection

https://github.com/mmaaz60/mvits_for_class_agnostic_od

Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection

https://github.com/rsCPSyEu/ovd_cod

Grounded Language-Image Pre-training

https://github.com/salman-h-khan/PL-ZSD_Release

Polarity Loss for Zero-shot Object Detection

https://github.com/salman-h-khan/ZSD_Release

Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts

https://github.com/sincerass/mvlpt

ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.