Few-Shot Object Detection
75 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: Research Advances and Challenges
This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years and summarize the existing challenges and solutions.
Few-shot Oriented Object Detection with Memorable Contrastive Learning in Remote Sensing Images
Few-shot object detection (FSOD) has garnered significant research attention in the field of remote sensing due to its ability to reduce the dependency on large amounts of annotated data.
Exploring Robust Features for Few-Shot Object Detection in Satellite Imagery
Moreover, we study the performance of both visual and image-text features, namely DINOv2 and CLIP, including two CLIP models specifically tailored for remote sensing applications.
Few-Shot Object Detection with Sparse Context Transformers
Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data.
Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples.
Stability Plasticity Decoupled Fine-tuning For Few-shot end-to-end Object Detection
Few-shot object detection(FSOD) aims to design methods to adapt object detectors efficiently with only few annotated samples.
GRSDet: Learning to Generate Local Reverse Samples for Few-shot Object Detection
By transferring the knowledge of IFC from the base training to fine-tuning, the IFC generates plentiful novel samples to calibrate the novel class distribution.
Decoupled DETR For Few-shot Object Detection
We also explore various types of skip connection between the encoder and decoder for DETR.
Object Detection in Aerial Images in Scarce Data Regimes
We demonstrate this with an in-depth analysis of existing FSOD methods on aerial images and observed a large performance gap compared to natural images.
ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection
Extensive experiments on the PASCAL VOC and COCO datasets show that our ECEA module can assist the few-shot detector to completely predict the object despite some regions failing to appear in the training samples and achieve the new state of the art compared with existing FSOD methods.
