[TOC]
- Title: One-Shot Object Detection With Co-Attention and Co-Excitation
- Author: Ting-I Hsieh et. al.
- Publish Year: Nov 2019
- Review Date: Mon, Oct 24, 2022
https://arxiv.org/pdf/1911.12529.pdf
Summary of paper
Motivation
- this paper aims to tackle the challenging problem of one-shot object detection, Given a query image patch whose class label is not included in the training data,
- To this end, they developed a novel co-attention and co-excitation (CoAE) framework that makes contributions in three key technical aspects
- first, use the non-local operation to explore the co-attention embodied in each query-target pair and yield region proposals accounting for the one-shot situation.
- second, we formulate a squeeze-and-co-excitation scheme that can adaptively emphasise correlated feature channels to help uncover relevant object proposals and eventually the target objects
- third, we design a margin-based ranking loss for implicitly learning a metric to predict the similarity of a region proposal to the underlying query, no matter its class label is seen or unseen training.
