Contrastive pretraining for semantic segmentation is robust to noisy positive pairs

TL;DR

This paper demonstrates that contrastive pretraining for semantic segmentation, especially in remote sensing, is surprisingly robust to noisy positive pairs, allowing for more flexible data pairing without performance loss.

Contribution

It reveals that domain-specific contrastive learning for semantic segmentation can tolerate or benefit from noisy positive pairs, reducing the need for careful pair filtering.

Findings

Robustness of downstream segmentation to noisy positive pairs

Benefits of including noisy pairs in pretraining datasets

Validation on remote sensing and synthetic datasets

Abstract

Domain-specific variants of contrastive learning can construct positive pairs from two distinct in-domain images, while traditional methods just augment the same image twice. For example, we can form a positive pair from two satellite images showing the same location at different times. Ideally, this teaches the model to ignore changes caused by seasons, weather conditions or image acquisition artifacts. However, unlike in traditional contrastive methods, this can result in undesired positive pairs, since we form them without human supervision. For example, a positive pair might consist of one image before a disaster and one after. This could teach the model to ignore the differences between intact and damaged buildings, which might be what we want to detect in the downstream task. Similar to false negative pairs, this could impede model performance. Crucially, in this setting only parts of the images differ in relevant ways, while other parts remain similar. Surprisingly, we find that downstream semantic segmentation is either robust to such badly matched pairs or even benefits from them. The experiments are conducted on the remote sensing dataset xBD, and a synthetic segmentation dataset for which we have full control over the pairing conditions. As a result, practitioners can use these domain-specific contrastive methods without having to filter their positive pairs beforehand, or might even be encouraged to purposefully include such pairs in their pretraining dataset.