Despite the growing demand for interactive AI systems, there have been few comprehensive studies on human-AI interaction in visual understanding e.g. segmentation. Inspired by the development of prompt-based universal interfaces for LLMs, this paper presents SEEM, a promptable, interactive model for Segmenting Everything Everywhere all at once in an image. SEEM has four desiderata: i) Versatility: by introducing a versatile prompting engine for different types of prompts, including points, boxes, scribbles, masks, texts, and referred regions of another image; ii) Compositionality: by learning a joint visual-semantic space for visual and textual prompts to compose queries on the fly for inference as shown in Fig 1; iii)Interactivity: by incorporating learnable memory prompts to retain dialog history information via mask-guided cross-attention; and iv) Semantic-awareness: by using a text encoder to encode text queries and mask labels for open-vocabulary segmentation.

翻译：尽管对交互式AI系统的需求日益增长，但针对视觉理解（如分割）中的人机交互研究仍相对匮乏。受大语言模型（LLM）中基于提示的通用交互界面发展启发，本文提出SEEM——一种可提示、交互式模型，能够一次性全方位分割图像中的一切内容。SEEM具备四项核心特征：i）多功能性：通过引入通用提示引擎支持多种提示类型，包括点、框、涂鸦、掩码、文本以及另一图像的参考区域；ii）组合性：通过学习联合视觉-语义空间，实时组合视觉与文本提示的查询（如图1所示）；iii）交互性：通过引入可学习记忆提示，利用掩码引导的交叉注意力机制保留对话历史信息；iv）语义感知性：使用文本编码器对文本查询和掩码标签进行编码，实现开放词汇分割。