Matching Multiple Perspectives for Efficient Representation Learning

TL;DR

This paper introduces a multi-perspective matching method combined with self-supervised learning to improve object representation quality, especially in data-scarce scenarios, demonstrated on robotic vacuum data.

Contribution

It proposes a novel approach integrating multi-view matching with self-supervised learning, enhancing representation learning from limited data.

Findings

Improved object classification accuracy with multi-view data

Effective self-supervised pretraining on robotic data

Enhanced holistic understanding of objects from multiple perspectives

Abstract

Representation learning approaches typically rely on images of objects captured from a single perspective that are transformed using affine transformations. Additionally, self-supervised learning, a successful paradigm of representation learning, relies on instance discrimination and self-augmentations which cannot always bridge the gap between observations of the same object viewed from a different perspective. Viewing an object from multiple perspectives aids holistic understanding of an object which is particularly important in situations where data annotations are limited. In this paper, we present an approach that combines self-supervised learning with a multi-perspective matching technique and demonstrate its effectiveness on learning higher quality representations on data captured by a robotic vacuum with an embedded camera. We show that the availability of multiple views of the same object combined with a variety of self-supervised pretraining algorithms can lead to improved object classification performance without extra labels.