The variety of objects in the real world is nearly unlimited and is thus
impossible to capture using models trained on a fixed set of categories. As a
result, in recent years, open-vocabulary methods have attracted the interest of
the community. This paper proposes a new method for zero-shot open-vocabulary
segmentation. Prior work largely relies on contrastive training using
image-text pairs, leveraging grouping mechanisms to learn image features that
are both aligned with language and well-localised. This however can introduce
ambiguity as the visual appearance of images with similar captions often
varies. Instead, we leverage the generative properties of large-scale
text-to-image diffusion models to sample a set of support images for a given
textual category. This provides a distribution of appearances for a given text
circumventing the ambiguity problem. We further propose a mechanism that
considers the contextual background of the sampled images to better localise
objects and segment the background directly. We show that our method can be
used to ground several existing pre-trained self-supervised feature extractors
in natural language and provide explainable predictions by mapping back to
regions in the support set. Our proposal is training-free, relying on
pre-trained components only, yet, shows strong performance on a range of
open-vocabulary segmentation benchmarks, obtaining a lead of more than 10% on
the Pascal VOC benchmark.

本文提出了一种基于大规模文本到图像扩散模型的零样本开放词库分割方法，通过考虑采样的图像的背景来更好地定位对象并直接分割背景，并证明该方法可以在多个基准测试中表现出色，在 Pascal VOC 基准测试上领先了超过 10%。