Few-Shot Object Detection
39 papers with code • 6 benchmarks • 5 datasets
Target: To detect objects of novel categories with just a few training samples.
A clear explanation of the few-shot object detection task and its differences with few-shot classification can be found in "A Survey of Self-Supervised and Few-Shot Object Detection": https://gabrielhuang.github.io/fsod-survey/
Benchmarks
These leaderboards are used to track progress in Few-Shot Object Detection
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Libraries
Use these libraries to find Few-Shot Object Detection models and implementations
COCO
LVIS
FSOD
LOGO-Net
Most implemented papers
Few-shot Object Detection via Feature Reweighting
bingykang/Fewshot_Detection
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ICCV 2019
The feature learner extracts meta features that are generalizable to detect novel object classes, using training data from base classes with sufficient samples.
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 with Attention-RPN and Multi-Relation Detector
fanq15/FewX
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CVPR 2020
To train our network, we contribute a new dataset that contains 1000 categories of various objects with high-quality annotations.
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited.
Frustratingly Simple Few-Shot Object Detection
ucbdrive/few-shot-object-detection
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ICML 2020
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 and Viewpoint Estimation for Objects in the Wild
YoungXIAO13/FewShotDetection
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ECCV 2020
In this paper, we tackle the problems of few-shot object detection and few-shot viewpoint estimation.
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
Few-shot object detection has made substantial progressby representing novel class objects using the feature representation learned upon a set of base class objects.
Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation
Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks.
Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment
guangxinghan/meta-faster-r-cnn
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To improve the fine-grained few-shot proposal classification, we propose a novel attentive feature alignment method to address the spatial misalignment between the noisy proposals and few-shot classes, thus improving the performance of few-shot object detection.
LSTD: A Low-Shot Transfer Detector for Object Detection
Second, we introduce a novel regularized transfer learning framework for low-shot detection, where the transfer knowledge (TK) and background depression (BD) regularizations are proposed to leverage object knowledge respectively from source and target domains, in order to further enhance fine-tuning with a few target images.
