Few-Shot Object Detection

66 papers with code • 6 benchmarks • 7 datasets

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.


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

Most implemented papers

ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

computer-vision-in-the-wild/cvinw_readings 19 Apr 2022

In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks.

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 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.

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.

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.

FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding

MegviiDetection/FSCE CVPR 2021

We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected objects.

Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection

liewfeng/imted ICCV 2023

Except for the backbone networks, however, other components such as the detector head and the feature pyramid network (FPN) remain trained from scratch, which hinders fully tapping the potential of representation models.

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.