computer-vision
Few-Shot Object Detection
3260 papers • 126 benchmarks • 313 datasets
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.
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in few-shot-object-detection-1
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Dataset
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Actions
MS-COCO (10-shot)
MS-COCO (10-shot)
CAMO-FS
CAMO-FS
MS-COCO (30-shot)
Libraries
iUse these libraries to find few-shot-object-detection-1 models and implementations
3 papers 15
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Most implemented papers
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
Jianfeng Gao, Houdong Hu, Zicheng Liu, Jianwei Yang, Pengchuan Zhang, Haotian Liu, Liunian Harold Li, J. Aneja, Ping Jin, Yong Jae Lee•Mon Apr 18 2022
This work builds ELEVATER (Evaluation of Language-augmented Visual Task-level Transfer), the first benchmark and toolkit for evaluating (pre-trained) language-AUgmented visual models.
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