Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest?…

Producing quality segmentation masks for images is a fundamental problem in computer
vision. Recent research has explored large-scale supervised training to enable zero-shot?…

Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a wo-stage
scheme. The general idea is to first generate class-agnostic region proposals and then feed?…

The emerging scale segmentation model, Segment Anything (SAM), exhibits impressive
capabilities in zero-shot segmentation for natural images. However, when applied to?…

Zero-shot semantic segmentation (ZS3) aims to segment the novel categories that have not
been seen in the training. Existing works formulate ZS3 as a pixel-level zero-shot?…

The emergence of CLIP has opened the way for open-world image perception. The zero-
shot classification capabilities of the model are impressive but are harder to use for dense?…

The deep learning methods in addressing semantic segmentation typically demand vast
amount of pixel-wise annotated training samples. In this work, we present zero-shot?…

Recently, pre-trained vision-language models have been increasingly used to tackle the
challenging zero-shot segmentation task. Typical solutions follow the paradigm of first?…

We have witnessed the explosive success of deep neural networks (DNNs). However, DNNs
typically assume a large amount of training data, and this is not always available in practical?…

We study universal zero-shot segmentation in this work to achieve panoptic, instance, and
semantic segmentation for novel categories without any training samples. Such zero-shot?…