In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities, but this idea is less explored in the vision domain. Existing visual prompting methods focus on referring segmentation to segment the most relevant object, falling short of addressing many generic vision tasks like open-set segmentation and detection. In this paper, we introduce a universal visual in-context prompting framework for both tasks. In particular, we build on top of an encoder-decoder architecture, and develop a versatile prompt encoder to support a variety of prompts like strokes, boxes, and points. We further enhance it to take an arbitrary number of reference image segments as the context. Our extensive explorations show that the proposed visual in-context prompting elicits extraordinary referring and generic segmentation capabilities to refer and detect, yielding competitive performance to close-set in-domain datasets and showing promising results on many open-set segmentation datasets. By joint training on COCO and SA-1B, our model achieves $57.7$ PQ on COCO and $23.2$ PQ on ADE20K. Code will be available at https://github.com/UX-Decoder/DINOv.

翻译：在大型语言模型（LLMs）中，上下文提示已成为提升零样本能力的常用方法，但该思想在视觉领域的研究尚不充分。现有视觉提示方法主要关注指代分割以分割最相关目标，难以应对开放集分割与检测等通用视觉任务。本文针对两类任务提出通用视觉上下文提示框架。具体而言，我们基于编码器-解码器架构，开发了支持笔触、边界框和点等多种提示的通用提示编码器，并进一步扩展其能力，使其可接受任意数量的参考图像片段作为上下文。大量实验表明，所提出的视觉上下文提示能够激发卓越的指代与泛化分割/检测能力，在闭集领域数据集上取得具有竞争力的性能，并在多个开放集分割数据集上表现优异。通过对COCO和SA-1B联合训练，我们的模型在COCO上达到57.7 PQ，在ADE20K上达到23.2 PQ。代码将开源至https://github.com/UX-Decoder/DINOv。