Studying Image Diffusion Features for Zero-Shot Video Object Segmentation

Thanos Delatolas; Vicky Kalogeiton; Dim P. Papadopoulos

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arXiv · 2504.05468

Studying Image Diffusion Features for Zero-Shot Video Object Segmentation

Thanos Delatolas·Vicky Kalogeiton·Dim P. Papadopoulos

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Abstract

This paper investigates the use of large-scale diffusion models for Zero-Shot Video Object Segmentation (ZS-VOS) without fine-tuning on video data or training on any image segmentation data. While diffusion models have demonstrated strong visual representations across various tasks, their direct application to ZS-VOS remains underexplored. Our goal is to find the optimal feature extraction process for ZS-VOS by identifying the most suitable time step and layer from which to extract features. We further analyze the affinity of these features and observe a strong correlation with point correspondences. Through extensive experiments on DAVIS-17 and MOSE, we find that diffusion models trained on ImageNet outperform those trained on larger, more diverse datasets for ZS-VOS. Additionally, we highlight the importance of point correspondences in achieving high segmentation accuracy, and we yield state-of-the-art results in ZS-VOS. Finally, our approach performs on par with models trained on expensive image segmentation datasets.

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Thanos Delatolas, Vicky Kalogeiton, Dim P. Papadopoulos. 2025-04-07. Studying Image Diffusion Features for Zero-Shot Video Object Segmentation. https://arxiv.org/abs/2504.05468

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