Transitive Invariance for Self-supervised Visual Representation Learning

TL;DR

This paper introduces a novel self-supervised learning approach that leverages a graph of objects and their invariances to learn more robust visual representations, improving performance on recognition tasks without relying on labeled data.

Contribution

It proposes organizing data with multiple invariances into a graph and applying transitivity to enhance self-supervised visual representation learning.

Findings

Achieves 63.2% mAP on PASCAL VOC 2007 with Fast R-CNN

Close to supervised performance on COCO dataset with 23.5% mAP

Outperforms ImageNet pre-trained network in surface normal estimation

Abstract

Learning visual representations with self-supervised learning has become popular in computer vision. The idea is to design auxiliary tasks where labels are free to obtain. Most of these tasks end up providing data to learn specific kinds of invariance useful for recognition. In this paper, we propose to exploit different self-supervised approaches to learn representations invariant to (i) inter-instance variations (two objects in the same class should have similar features) and (ii) intra-instance variations (viewpoint, pose, deformations, illumination, etc). Instead of combining two approaches with multi-task learning, we argue to organize and reason the data with multiple variations. Specifically, we propose to generate a graph with millions of objects mined from hundreds of thousands of videos. The objects are connected by two types of edges which correspond to two types of invariance: "different instances but a similar viewpoint and category" and "different viewpoints of the same instance". By applying simple transitivity on the graph with these edges, we can obtain pairs of images exhibiting richer visual invariance. We use this data to train a Triplet-Siamese network with VGG16 as the base architecture and apply the learned representations to different recognition tasks. For object detection, we achieve 63.2% mAP on PASCAL VOC 2007 using Fast R-CNN (compare to 67.3% with ImageNet pre-training). For the challenging COCO dataset, our method is surprisingly close (23.5%) to the ImageNet-supervised counterpart (24.4%) using the Faster R-CNN framework. We also show that our network can perform significantly better than the ImageNet network in the surface normal estimation task.