HyperAIHyperAI

Command Palette

Search for a command to run...

Console

Few Shot Object Detection On Ms Coco 10 Shot

Metrics

AP

Results

Performance results of various models on this benchmark

Paper Title
CD-ViTO35.3Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
DE-ViT34.0Detect Everything with Few Examples
BIOT26.3Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners
RISF (SWIN-Large)25.5Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection
DETReg-ft-full DDETR25DETReg: Unsupervised Pretraining with Region Priors for Object Detection
imTED+ViT-B22.5Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection
hANMCL22.4Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning
RISF (Resnet-101)21.9Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection
DCFS19.5Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
CFA-DeFRCN19.1CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection
DAnA-FasterRCNN18.6Dual-Awareness Attention for Few-Shot Object Detection
DeFRCN18.5DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection
Meta-DETR (Multi-Scale Feature)17.8Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation
Meta-DETR (Single-Scale Feature)16.7Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation
FSRN (RetinaNet)15.8Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors
CME15.1Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
imTED+ViT-S15.0Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection
FSDetView + PSP13.4Few-Shot Object Detection by Attending to Per-Sample-Prototype
PnP-FSOD + CT13.3Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization Inference
FSDetView12.5Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild
0 of 32 row(s) selected.