DeepAI AI Chat
Log In Sign Up

Meta R-CNN : Towards General Solver for Instance-level Low-shot Learning

09/28/2019
by   Xiaopeng Yan, et al.
IEEE
SUN YAT-SEN UNIVERSITY
0

Resembling the rapid learning capability of human, low-shot learning empowers vision systems to understand new concepts by training with few samples. Leading approaches derived from meta-learning on images with a single visual object. Obfuscated by a complex background and multiple objects in one image, they are hard to promote the research of low-shot object detection/segmentation. In this work, we present a flexible and general methodology to achieve these tasks. Our work extends Faster /Mask R-CNN by proposing meta-learning over RoI (Region-of-Interest) features instead of a full image feature. This simple spirit disentangles multi-object information merged with the background, without bells and whistles, enabling Faster /Mask R-CNN turn into a meta-learner to achieve the tasks. Specifically, we introduce a Predictor-head Remodeling Network (PRN) that shares its main backbone with Faster /Mask R-CNN. PRN receives images containing low-shot objects with their bounding boxes or masks to infer their class attentive vectors. The vectors take channel-wise soft-attention on RoI features, remodeling those R-CNN predictor heads to detect or segment the objects that are consistent with the classes these vectors represent. In our experiments, Meta R-CNN yields the state of the art in low-shot object detection and improves low-shot object segmentation by Mask R-CNN.

READ FULL TEXT

page 3

page 7

04/15/2021

Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment

Few-shot object detection (FSOD) aims to detect objects using only few e...
07/30/2022

Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation

Few-shot object detection has been extensively investigated by incorpora...
12/05/2018

Few-shot Object Detection via Feature Reweighting

This work aims to solve the challenging few-shot object detection proble...
03/18/2020

CAFENet: Class-Agnostic Few-Shot Edge Detection Network

We tackle a novel few-shot learning challenge, which we call few-shot se...
09/27/2021

Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images

This paper proposes a few-shot method based on Faster R-CNN and represen...
01/30/2020

Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion

Images of heavily occluded objects in cluttered scenes, such as fruit cl...