Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task

TL;DR

This paper introduces E-DIY, a self-supervised learning mechanism that enhances visual pre-training by promoting diversity and invariance in region-level features, leading to improved transfer performance.

Contribution

E-DIY is a novel approach that explicitly encourages diversity among different regions and invariance across augmented views, addressing representation collapse in self-supervised learning.

Findings

Achieves 2.1% improvement over BYOL on COCO object detection.

Effectively preserves multi-grained visual information inside images.

Demonstrates superior transfer performance on downstream tasks.

Abstract

Recently, self-supervised learning methods have achieved remarkable success in visual pre-training task. By simply pulling the different augmented views of each image together or other novel mechanisms, they can learn much unsupervised knowledge and significantly improve the transfer performance of pre-training models. However, these works still cannot avoid the representation collapse problem, i.e., they only focus on limited regions or the extracted features on totally different regions inside each image are nearly the same. Generally, this problem makes the pre-training models cannot sufficiently describe the multi-grained information inside images, which further limits the upper bound of their transfer performance. To alleviate this issue, this paper introduces a simple but effective mechanism, called Exploring the Diversity and Invariance in Yourself E-DIY. By simply pushing the most different regions inside each augmented view away, E-DIY can preserve the diversity of extracted region-level features. By pulling the most similar regions from different augmented views of the same image together, E-DIY can ensure the robustness of region-level features. Benefited from the above diversity and invariance exploring mechanism, E-DIY maximally extracts the multi-grained visual information inside each image. Extensive experiments on downstream tasks demonstrate the superiority of our proposed approach, e.g., there are 2.1% improvements compared with the strong baseline BYOL on COCO while fine-tuning Mask R-CNN with the R50-C4 backbone and 1X learning schedule.