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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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Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion
NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection
Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection
Multimodal Reference Visual Grounding
Context in object detection: a systematic literature review
Exploring Few-Shot Object Detection on Blood Smear Images: A Case Study of Leukocytes and Schistocytes
Visual-RFT: Visual Reinforcement Fine-Tuning
Multi-Perspective Data Augmentation for Few-shot Object Detection
Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment
Generalization-Enhanced Few-Shot Object Detection in Remote Sensing
SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object Detection
AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks
UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation
Open-vocabulary vs. Closed-set: Best Practice for Few-shot Object Detection Considering Text Describability
FUSED-Net: Detecting Traffic Signs with Limited Data
Beyond Few-shot Object Detection: A Detailed Survey
A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection
PS-TTL: Prototype-based Soft-labels and Test-Time Learning for Few-shot Object Detection
SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection
Semantic Enhanced Few-shot Object Detection
The Solution for CVPR2024 Foundational Few-Shot Object Detection Challenge
InfRS: Incremental Few-Shot Object Detection in Remote Sensing Images
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
UniFS: Universal Few-shot Instance Perception with Point Representations
Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images
Few-Shot Object Detection: Research Advances and Challenges
AirShot: Efficient Few-Shot Detection for Autonomous Exploration
Cross-domain Multi-modal Few-shot Object Detection via Rich Text
Few-shot Oriented Object Detection with Memorable Contrastive Learning in Remote Sensing Images
Exploring Robust Features for Few-Shot Object Detection in Satellite Imagery
Few-Shot Object Detection with Sparse Context Transformers
Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
Stability Plasticity Decoupled Fine-tuning For Few-shot end-to-end Object Detection
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