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Few-Shot Object Detection

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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.

논문

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Fine-Grained Prototypes Distillation for Few-Shot Object Detection

GRSDet: Learning to Generate Local Reverse Samples for Few-shot Object Detection

Revisiting Few-Shot Object Detection with Vision-Language Models

TIDE: Test Time Few Shot Object Detection

Decoupled DETR For Few-shot Object Detection

Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection

Object Detection in Aerial Images in Scarce Data Regimes

Detect Everything with Few Examples

Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects

ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection

Few-Shot Object Detection via Synthetic Features with Optimal Transport

Improved Region Proposal Network for Enhanced Few-Shot Object Detection

Aligned Unsupervised Pretraining of Object Detectors with Self-training

Cos R-CNN for Online Few-shot Object Detection

Rethinking Intersection Over Union for Small Object Detection in Few-Shot Regime

Multi-modal Queried Object Detection in the Wild

Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection

The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation

Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection

Explore the Power of Synthetic Data on Few-shot Object Detection

Identification of Novel Classes for Improving Few-Shot Object Detection

DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection

Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images

LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object Detection

NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging

An Effective Crop-Paste Pipeline for Few-shot Object Detection

Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach

Few-Shot Object Detection via Variational Feature Aggregation

Explore the Power of Dropout on Few-shot Learning

Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation

Proposal Distribution Calibration for Few-Shot Object Detection

Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection

Multi-Modal Few-Shot Temporal Action Detection

Few-shot Object Detection with Refined Contrastive Learning

Spatial Reasoning for Few-Shot Object Detection

Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection

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