We introduce **SEEM** that can **S**egment **E**verything **E**verywhere with **M**ulti-modal prompts all at once. SEEM allows users to easily segment an image using prompts of different types including visual prompts (points, marks, boxes, scribbles and image segments) and language prompts (text and audio), etc. It can also work with any combination of prompts or generalize to custom prompts!
Inspired by the appealing universal interface in LLMs, we are advocating universal, interactive multi-modal interface for any types of segmentation with **ONE SINGLE MODEL**. We emphasize **4** important features of **SEEM** here.
Compared with [SAM](https://arxiv.org/abs/2304.02643), SEEM has the following strengths. First, SEEM has a Unified prompt encoder that encode all visual and language prompts into a joint representation space. In consequence, SEEM has more general usage. It has potential to extend to custom prompts. Second, SEEM do very well on text to mask (grounding segmentation) and output semantic-aware predictions.
This figure shows a comparison of our model with concurrent work SAM on the level of interactions and semantics. The x-axis and y-axis denote the level of interaction and semantics, respectively. Three segmentation tasks are shown which are Open-set Segmentation, Edge detection, and Interactive Segmentation. They have different levels of interactions and semantics. For example, Open-set Segmentation usually requires a high level of semantics and does not require interaction. Compared with SAM, our model covers a larger range in both interaction and semantics levels. For example, SAM only supports limited interaction types like points and boxes and it does not support high-semantic tasks since it does not output semantic labels itself. Note that although we do not report edge detection results, our model can support it by simply converting masks to edges.
