Segment Everything Everywhere All at Once - Summary

SEEM is an innovative model designed for interactive image segmentation, capable of handling various types of prompts such as points, boxes, scribbles, masks, texts, and referred regions from other images, allowing for comprehensive segmentation across an entire image simultaneously. It features a versatile prompting engine and a lightweight prompt decoder that efficiently manages multiple rounds of interactions, demonstrating a robust ability to adapt to new user intents. The model's effectiveness is supported by a detailed empirical study conducted across various segmentation tasks. Furthermore, SEEM incorporates technologies like GPT, T5, DETR, CLIP, and X-Decoder to enhance its visual understanding and semantic segmentation capabilities.