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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>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Robust Region Feature Synthesizer for Zero-Shot Object Detection
Grounded Language-Image Pre-training
A Survey of Deep Learning for Low-Shot Object Detection
Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask Architecture
Zero-shot Object Detection Through Vision-Language Embedding Alignment
Semantics-Guided Contrastive Network for Zero-Shot Object detection
Learning Open-World Object Proposals without Learning to Classify
Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
Zero-Shot Instance Segmentation
Synthesizing the Unseen for Zero-shot Object Detection
Background Learnable Cascade for Zero-Shot Object Detection
GTNet: Generative Transfer Network for Zero-Shot Object Detection
Image Captioning with Unseen Objects
Polarity Loss for Zero-shot Object Detection
Zero-Shot Object Detection by Hybrid Region Embedding
Zero-Shot Object Detection
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.