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SOTA
Few-Shot-Objekterkennung
Few Shot Object Detection On Ms Coco 10 Shot
Few Shot Object Detection On Ms Coco 10 Shot
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AP
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
AP
Paper Title
Repository
MetaYOLO
5.6
Few-shot Object Detection via Feature Reweighting
MPSR
9.8
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
imTED+ViT-S
15.0
Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection
DeFRCN
18.5
DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection
CFA-DeFRCN
19.1
CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection
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FSDetView
12.5
Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild
SSR-FSD
11.3
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection
-
BIOT
26.3
Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners
-
DAnA-FasterRCNN
18.6
Dual-Awareness Attention for Few-Shot Object Detection
FSOD(Universal-Prototype)
11.0
Universal-Prototype Enhancing for Few-Shot Object Detection
FSDetView + PSP
13.4
Few-Shot Object Detection by Attending to Per-Sample-Prototype
-
DCFS
19.5
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
Meta-DETR (Single-Scale Feature)
16.7
Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation
CME
15.1
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
RISF (Resnet-101)
21.9
Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection
FSRN (RetinaNet)
15.8
Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors
-
Meta-DETR (Multi-Scale Feature)
17.8
Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation
RISF (SWIN-Large)
25.5
Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection
imTED+ViT-B
22.5
Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection
DE-ViT
34.0
Detect Everything with Few Examples
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