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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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A Unified Framework with Meta-dropout for Few-shot Learning
CD-FSOD: A Benchmark for Cross-domain Few-shot Object Detection
Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors
FS-DETR: Few-Shot DEtection TRansformer with prompting and without re-training
Fast Hierarchical Learning for Few-Shot Object Detection
Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method
Exploring Effective Knowledge Transfer for Few-shot Object Detection
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation
Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representations
VizWiz-FewShot: Locating Objects in Images Taken by People With Visual Impairments
Few-shot Object Counting and Detection
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection
0/1 Deep Neural Networks via Block Coordinate Descent
Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection
Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning
MINI: Mining Implicit Novel Instances for Few-Shot Object Detection
Few-Shot Object Detection with Proposal Balance Refinement
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting
Few-Shot Object Detection in Unseen Domains
CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection
Exploring Plain Vision Transformer Backbones for Object Detection
Few-Shot Object Detection with Fully Cross-Transformer
Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison
Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection
Efficient Few-Shot Object Detection via Knowledge Inheritance
UnseenNet: Fast Training Detector for Any Unseen Concept
Mobile Robot Manipulation using Pure Object Detection
Detecting Twenty-thousand Classes using Image-level Supervision
A Unified Framework for Attention-Based Few-Shot Object Detection
Few-Shot Object Detection: A Comprehensive Survey
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