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Few-Shot Object Detection
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Few-Shot Object Detection is a computer vision task that involves detecting objects in images with limited training data. The goal is to train a model on a few examples of each object class and then use the model to detect objects in new images.
논문
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Few-shot Object Detection with Feature Attention Highlight Module in Remote Sensing Images
Few-Shot Object Detection via Knowledge Transfer
Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation
Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object Detection
Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
Top-Related Meta-Learning Method for Few-Shot Object Detection
Few-shot Object Detection on Remote Sensing Images
Incremental Few-Shot Object Detection for Robotics
Frustratingly Simple Few-Shot Object Detection
Context-Transformer: Tackling Object Confusion for Few-Shot Detection
StarNet: towards Weakly Supervised Few-Shot Object Detection
Incremental Few-Shot Object Detection
Meta-learning algorithms for Few-Shot Computer Vision
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning
Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector
Few-shot Object Detection via Feature Reweighting
One-Shot Instance Segmentation
Scalable Logo Recognition using Proxies
Multidomain Document Layout Understanding using Few Shot Object Detection
RepMet: Representative-based metric learning for classification and one-shot object detection
LSTD: A Low-Shot Transfer Detector for Object Detection
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