SOTAVerified

Few-Shot Object Detection

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.

Papers

Showing 150 of 179 papers

TitleStatusHype
No time to train! Training-Free Reference-Based Instance SegmentationCode3
Decoupling Classifier for Boosting Few-shot Object Detection and Instance SegmentationCode1
CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature ConfusionCode1
NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and ResultsCode2
Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object DetectionCode2
Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object DetectionCode2
Multimodal Reference Visual Grounding0
Context in object detection: a systematic literature review0
Exploring Few-Shot Object Detection on Blood Smear Images: A Case Study of Leukocytes and Schistocytes0
Visual-RFT: Visual Reinforcement Fine-TuningCode7
Multi-Perspective Data Augmentation for Few-shot Object DetectionCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
Generalization-Enhanced Few-Shot Object Detection in Remote SensingCode1
SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object DetectionCode1
AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks0
UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation0
Open-vocabulary vs. Closed-set: Best Practice for Few-shot Object Detection Considering Text DescribabilityCode0
FUSED-Net: Detecting Traffic Signs with Limited Data0
Beyond Few-shot Object Detection: A Detailed Survey0
A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection0
PS-TTL: Prototype-based Soft-labels and Test-Time Learning for Few-shot Object DetectionCode1
SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object DetectionCode1
Semantic Enhanced Few-shot Object Detection0
The Solution for CVPR2024 Foundational Few-Shot Object Detection Challenge0
Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners0
InfRS: Incremental Few-Shot Object Detection in Remote Sensing ImagesCode1
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object DetectionCode7
UniFS: Universal Few-shot Instance Perception with Point RepresentationsCode1
Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images0
Few-Shot Object Detection: Research Advances and Challenges0
AirShot: Efficient Few-Shot Detection for Autonomous ExplorationCode0
Cross-domain Multi-modal Few-shot Object Detection via Rich TextCode0
Few-shot Oriented Object Detection with Memorable Contrastive Learning in Remote Sensing Images0
Exploring Robust Features for Few-Shot Object Detection in Satellite ImageryCode1
Few-Shot Object Detection with Sparse Context Transformers0
Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object DetectorCode2
Stability Plasticity Decoupled Fine-tuning For Few-shot end-to-end Object Detection0
Fine-Grained Prototypes Distillation for Few-Shot Object DetectionCode2
Few-Shot Object Detection with Foundation Models0
SNIDA: Unlocking Few-Shot Object Detection with Non-linear Semantic Decoupling Augmentation0
GRSDet: Learning to Generate Local Reverse Samples for Few-shot Object Detection0
Revisiting Few-Shot Object Detection with Vision-Language ModelsCode0
TIDE: Test Time Few Shot Object DetectionCode0
Decoupled DETR For Few-shot Object Detection0
Re-Scoring Using Image-Language Similarity for Few-Shot Object DetectionCode1
Object Detection in Aerial Images in Scarce Data Regimes0
Detect Everything with Few ExamplesCode2
Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel ObjectsCode1
ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection0
Few-Shot Object Detection via Synthetic Features with Optimal TransportCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Training-freeAP36.6Unverified
2CD-ViTOAP35.3Unverified
3DE-ViTAP34Unverified
4BIOTAP26.3Unverified
5RISF (SWIN-Large)AP25.5Unverified
6DETReg-ft-full DDETRAP25Unverified
7imTED+ViT-BAP22.5Unverified
8hANMCLAP22.4Unverified
9RISF (Resnet-101)AP21.9Unverified
10DCFSAP19.5Unverified
#ModelMetricClaimedVerifiedStatus
1FS-CDIS (iFS-RCNN+ITL 5-shot)box AP10.36Unverified
2FS-CDIS (MTFA+IMS 5-shot)box AP10.36Unverified
3FS-CDIS (Res101-MTFA+IMS 5-shot)box AP10.36Unverified
4FS-CDIS (Res101-MTFA+ITL 5-shot)box AP9.76Unverified
5FS-CDIS (M-RCNN+ITL 5-shot)box AP9.67Unverified
6FS-CDIS (M-RCNN+IMS 5-shot)box AP9.52Unverified
7FS-CDIS (iFS-RCNN+IMS 5-shot)box AP8.44Unverified
8FS-CDIS (M-RCNN+IMS 3-shot)box AP7.96Unverified
9FS-CDIS (M-RCNN+ITL 3-shot)box AP7.85Unverified
10FS-CDIS (M-RCNN+ITL 2-shot)box AP7.56Unverified
#ModelMetricClaimedVerifiedStatus
1Training-freeAP36.8Unverified
2CD-ViTOAP35.9Unverified
3DE-ViTAP34Unverified
4BIOTAP33.8Unverified
5RISF (SWIN-Large)AP31.9Unverified
6imTED+ViT-BAP30.2Unverified
7DETReg-ft-full DDETRAP30Unverified
8hANMCLAP25Unverified
9RISF (Resnet-101)AP24.4Unverified
10Meta-DETR (Multi-Scale Feature)AP22.9Unverified
#ModelMetricClaimedVerifiedStatus
1best_single_model_valAP47.55Unverified
2htcAP39.05Unverified
3Organizer Provided BaselineAP27.26Unverified
4nullAP25.8Unverified
5Forest R-CNNAP23.2Unverified
6personAP21.82Unverified
7test balloon 6AP16.62Unverified
#ModelMetricClaimedVerifiedStatus
1Training-freeAP26.5Unverified
2hANMCLAP13.4Unverified
3UniFSAP12.7Unverified
4RISFAP11.7Unverified
5DCFSAP10Unverified
6DeFRCNAP9.3Unverified
7DCFSAP8.1Unverified
#ModelMetricClaimedVerifiedStatus
1TestConsistencyAP48.58Unverified
2ps4AP39.67Unverified
3Asynchronous SSLAP37.72Unverified
4CenterNet2AP35.84Unverified
5Organizer Provided BaselineAP26.86Unverified
#ModelMetricClaimedVerifiedStatus
1Grounding DINO 1.5 ProAverage Score66.3Unverified
2MQ-GLIP-TAverage Score57Unverified
3GLIP-TAverage Score50.7Unverified
#ModelMetricClaimedVerifiedStatus
1Grounding DINO 1.5 ProAverage Score54.7Unverified
2MQ-GLIP-TAverage Score43Unverified
3GLIP-TAverage Score38.9Unverified
#ModelMetricClaimedVerifiedStatus
1DETReg (ours)AP30Unverified
#ModelMetricClaimedVerifiedStatus
1UniFSAP18.2Unverified