SeeDiff: Off-the-Shelf Seeded Mask Generation from Diffusion Models

TL;DR

SeeDiff leverages attention mechanisms in Stable Diffusion to generate high-quality semantic segmentation masks without extra training, prompt tuning, or pre-trained models, by extracting initial seeds and expanding them through self-attention.

Contribution

This work introduces SeeDiff, a novel method that exploits attention in diffusion models for off-the-shelf semantic mask generation without additional training.

Findings

Achieves high-quality masks without training or prompt tuning.

Utilizes cross-attention for initial object seeds.

Expands seeds using multi-scale self-attention for full object coverage.

Abstract

Entrusted with the goal of pixel-level object classification, the semantic segmentation networks entail the laborious preparation of pixel-level annotation masks. To obtain pixel-level annotation masks for a given class without human efforts, recent few works have proposed to generate pairs of images and annotation masks by employing image and text relationships modeled by text-to-image generative models, especially Stable Diffusion. However, these works do not fully exploit the capability of text-guided Diffusion models and thus require a pre-trained segmentation network, careful text prompt tuning, or the training of a segmentation network to generate final annotation masks. In this work, we take a closer look at attention mechanisms of Stable Diffusion, from which we draw connections with classical seeded segmentation approaches. In particular, we show that cross-attention alone provides very coarse object localization, which however can provide initial seeds. Then, akin to region expansion in seeded segmentation, we utilize the semantic-correspondence-modeling capability of self-attention to iteratively spread the attention to the whole class from the seeds using multi-scale self-attention maps. We also observe that a simple-text-guided synthetic image often has a uniform background, which is easier to find correspondences, compared to complex-structured objects. Thus, we further refine a mask using a more accurate background mask. Our proposed method, dubbed SeeDiff, generates high-quality masks off-the-shelf from Stable Diffusion, without additional training procedure, prompt tuning, or a pre-trained segmentation network.