DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut

Paul Couairon, Mustafa Shukor, Jean-Emmanuel HAUGEARD, Matthieu Cord, Nicolas THOME

Keywords: Segmentation, Image Segmentation, Zero-Shot Segmentation, Diffusion Features, Normalized Cut

TL;DR: We propose DiffCut, a novel zero-shot image segmentation method based on a recursive normalized cut algorithm to segment diffusion features

Abstract: Foundation models have emerged as powerful tools across various domains including language, vision, and multimodal tasks. While prior works have addressed unsupervised semantic segmentation, they significantly lag behind supervised models. In this paper, we use a diffusion UNet encoder as a foundation vision encoder and introduce DiffCut, an unsupervised zero-shot segmentation method that solely harnesses the output features from the final self-attention block. Through extensive experimentation, we demonstrate that using these diffusion features in a graph based segmentation algorithm, significantly outperforms previous state-of-the-art methods on zero-shot segmentation. Specifically, we leverage a recursive Normalized Cut algorithm that regulates the granularity of detected objects and produces well-defined segmentation maps that precisely capture intricate image details. Our work highlights the remarkably accurate semantic knowledge embedded within diffusion UNet encoders that could then serve as foundation vision encoders for downstream tasks.

Primary Area: Machine vision

Submission Number: 3966