Optical Flow boosts Unsupervised Localization and Segmentation

TL;DR

This paper introduces a motion-based loss function leveraging optical flow to improve unsupervised object localization and segmentation, outperforming existing methods without requiring labeled data.

Contribution

It proposes a novel optical flow-based loss for fine-tuning vision transformers, enhancing unsupervised segmentation and localization performance.

Findings

Outperforms state-of-the-art unsupervised segmentation methods

Improves object localization accuracy in benchmarks

Enhances ViT features using motion cues

Abstract

Unsupervised localization and segmentation are long-standing robot vision challenges that describe the critical ability for an autonomous robot to learn to decompose images into individual objects without labeled data. These tasks are important because of the limited availability of dense image manual annotation and the promising vision of adapting to an evolving set of object categories in lifelong learning. Most recent methods focus on using visual appearance continuity as object cues by spatially clustering features obtained from self-supervised vision transformers (ViT). In this work, we leverage motion cues, inspired by the common fate principle that pixels that share similar movements tend to belong to the same object. We propose a new loss term formulation that uses optical flow in unlabeled videos to encourage self-supervised ViT features to become closer to each other if their corresponding spatial locations share similar movements, and vice versa. We use the proposed loss function to finetune vision transformers that were originally trained on static images. Our fine-tuning procedure outperforms state-of-the-art techniques for unsupervised semantic segmentation through linear probing, without the use of any labeled data. This procedure also demonstrates increased performance over original ViT networks across unsupervised object localization and semantic segmentation benchmarks.