Few-Shot Object Detection
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Greatest papers with code
Few-shot Object Detection via Feature Reweighting
ICCV 2019
• bingykang/Fewshot_Detection
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The feature learner extracts meta features that are generalizable to detect novel object classes, using training data from base classes with sufficient samples.
FEW-SHOT LEARNING FEW-SHOT OBJECT DETECTION IMAGE CLASSIFICATION
One-Shot Instance Segmentation
• bethgelab/siamese-mask-rcnn
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We demonstrate empirical results on MS Coco highlighting challenges of the one-shot setting: while transferring knowledge about instance segmentation to novel object categories works very well, targeting the detection network towards the reference category appears to be more difficult.
FEW-SHOT OBJECT DETECTION ONE-SHOT INSTANCE SEGMENTATION ONE-SHOT LEARNING ONE-SHOT OBJECT DETECTION
Frustratingly Simple Few-Shot Object Detection
• ucbdrive/few-shot-object-detection
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Such a simple approach outperforms the meta-learning methods by roughly 2~20 points on current benchmarks and sometimes even doubles the accuracy of the prior methods.
Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector
To train our network, we contribute a new dataset that contains 1000 categories of various objects with high-quality annotations.
Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning
Resembling the rapid learning capability of human, low-shot learning empowers vision systems to understand new concepts by training with few samples.
FEW-SHOT OBJECT DETECTION META-LEARNING SEMANTIC SEGMENTATION
RepMet: Representative-based metric learning for classification and one-shot object detection
• jshtok/RepMet
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Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples.
FEW-SHOT OBJECT DETECTION METRIC LEARNING OBJECT CLASSIFICATION ONE-SHOT OBJECT DETECTION
Meta-learning algorithms for Few-Shot Computer Vision
Few-Shot Learning is the challenge of training a model with only a small amount of data.
Context-Transformer: Tackling Object Confusion for Few-Shot Detection
• Ze-Yang/Context-Transformer
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Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors.
FEW-SHOT LEARNING FEW-SHOT OBJECT DETECTION TRANSFER LEARNING