Few-Shot Object Detection
72 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.
Libraries
Use these libraries to find Few-Shot Object Detection models and implementationsMost implemented papers
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
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.
Grounded Language-Image Pre-training
The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representation semantic-rich.
Mobile Robot Manipulation using Pure Object Detection
We develop an end-to-end manipulation method based solely on detection and introduce Task-focused Few-shot Object Detection (TFOD) to learn new objects and settings.
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.
RepMet: Representative-based metric learning for classification and one-shot object detection
Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples.
RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection
Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples.
Meta-learning algorithms for Few-Shot Computer Vision
Few-Shot Learning is the challenge of training a model with only a small amount of data.
Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning
Resembling the rapid learning capability of human, low-shot learning empowers vision systems to understand new concepts by training with few samples.
StarNet: towards Weakly Supervised Few-Shot Object Detection
Few-shot detection and classification have advanced significantly in recent years.