Few-Shot Object Detection
77 papers with code • 8 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.
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
MS COCO
LVIS
FSOD
ELEVATER
VizWiz-FewShot
Latest papers with no code
Few-Shot Object Detection via Synthetic Features with Optimal Transport
Our overarching goal is to train a generator that captures the data variations of the base dataset.
SimDETR: Simplifying self-supervised pretraining for DETR
DETR-based object detectors have achieved remarkable performance but are sample-inefficient and exhibit slow convergence.
Cos R-CNN for Online Few-shot Object Detection
We propose Cos R-CNN, a simple exemplar-based R-CNN formulation that is designed for online few-shot object detection.
Rethinking Intersection Over Union for Small Object Detection in Few-Shot Regime
SIoU improves small object detection in the non-few-shot regime, but this setting is unrealistic in the industry as annotated detection datasets are often too expensive to acquire.
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection
Few-shot object detection, the problem of modelling novel object detection categories with few training instances, is an emerging topic in the area of few-shot learning and object detection.
Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection
To mitigate this issue, we propose a novel variational autoencoder (VAE) based data generation model, which is capable of generating data with increased crop-related diversity.
Explore the Power of Synthetic Data on Few-shot Object Detection
To construct a representative synthetic training dataset, we maximize the diversity of the selected images via a sample-based and cluster-based method.
Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images
Object detection in remote sensing images relies on a large amount of labeled data for training.
NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging
Our contribution is three-fold: (1) we design a standalone lightweight generator with (2) class-wise heads (3) to generate and replay diverse instance-level base features to the RoI head while finetuning on the novel data.
An Effective Crop-Paste Pipeline for Few-shot Object Detection
Specifically, we first discover the base images which contain the FP of novel categories and select a certain amount of samples from them for the base and novel categories balance.
