Cooperating RPN's Improve Few-Shot Object Detection

11/19/2020
by   Weilin Zhang, et al.
0

Learning to detect an object in an image from very few training examples - few-shot object detection - is challenging, because the classifier that sees proposal boxes has very little training data. A particularly challenging training regime occurs when there are one or two training examples. In this case, if the region proposal network (RPN) misses even one high intersection-over-union (IOU) training box, the classifier's model of how object appearance varies can be severely impacted. We use multiple distinct yet cooperating RPN's. Our RPN's are trained to be different, but not too different; doing so yields significant performance improvements over state of the art for COCO and PASCAL VOC in the very few-shot setting. This effect appears to be independent of the choice of classifier or dataset.

READ FULL TEXT
research
05/04/2021

Hallucination Improves Few-Shot Object Detection

Learning to detect novel objects from few annotated examples is of great...
research
07/17/2023

Rethinking Intersection Over Union for Small Object Detection in Few-Shot Regime

In Few-Shot Object Detection (FSOD), detecting small objects is extremel...
research
11/02/2022

Spatial Reasoning for Few-Shot Object Detection

Although modern object detectors rely heavily on a significant amount of...
research
08/15/2023

Improved Region Proposal Network for Enhanced Few-Shot Object Detection

Despite significant success of deep learning in object detection tasks, ...
research
04/22/2022

Few-Shot Object Detection with Proposal Balance Refinement

Few-shot object detection has gained significant attention in recent yea...
research
06/26/2017

Few-shot Object Detection

In this paper, we study object detection using a large pool of unlabeled...
research
04/04/2017

ME R-CNN: Multi-Expert R-CNN for Object Detection

We introduce Multi-Expert Region-based CNN (ME R-CNN) which is equipped ...

Please sign up or login with your details

Forgot password? Click here to reset