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/


Use these libraries to find Few-Shot Object Detection models and implementations

Most implemented papers

Few-shot Object Detection via Feature Reweighting

bingykang/Fewshot_Detection 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 28 Nov 2018

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 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

jiaxi-wu/MPSR ECCV 2020

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 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 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

Bohao-Lee/CME CVPR 2021

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

ZhangGongjie/Meta-DETR 22 Mar 2021

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 15 Apr 2021

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

Cassie94/LSTD 5 Mar 2018

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.