Project Overview: Segment Everything Everywhere All at Once (SEEM)
Introduction
SEEM is a groundbreaking technological innovation that simplifies and enhances the image segmentation process. Developed by a team of researchers, SEEM provides users with the ability to segment images using various types of prompts all at once. This includes visual prompts such as points, marks, boxes, and scribbles, as well as language prompts in text and audio formats.
Key Features of SEEM
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Versatility: SEEM can handle different types of prompts like clicks, boxes, polygons, and even textual or audio inputs. This allows for a broad range of applications and flexibility in how images are segmented.
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Compositionality: It can process any combination of prompts, meaning multiple input types can be used together for more complex segmentation tasks.
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Interactivity: SEEM is designed for interactive use, allowing users to engage in multiple rounds of input. This is facilitated by a memory feature that recalls previous session information.
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Semantic Awareness: SEEM applies semantic labeling to any resulting image mask, enhancing the depth and accuracy of segmentation tasks.
Usage and Applications
SEEM is designed to be user-friendly. Users can start by uploading an image and choosing their preferred type of prompt. The model currently supports the COCO dataset vocabulary for 80 categories but can be expanded with custom labels as needed. Once the prompts are set, the system processes the image and provides segmented outputs with semantic labels.
Examples and Demonstrations
The versatility of SEEM is showcased through various examples:
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Click and Scribble to Mask: With minimal input like a click or a scribble, SEEM can generate a segmented image with categorized labels.
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Text to Mask: Users can input text to prompt the model to generate a mask, showcasing SEEM's ability to understand and process multi-modal inputs.
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Referring Image to Mask: By clicking on a reference image, SEEM can identify and segment similar objects in a target image, demonstrating its understanding of spatial and semantic relationships.
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Audio to Mask: SEEM can convert audio inputs to text with the help of Whisper, further broadening its applicability and interaction modality.
In addition to its core functionality, SEEM supports video segmentation without additional video training data, showcasing its power in handling dynamic content effectively.
Advancements and Updates
SEEM has seen a series of updates and applications that further demonstrate its capabilities:
- It has been integrated into systems like LLaVA-Interactive for image chat, segmentation, and editing.
- Utilization in new visual prompting techniques for improved generation with GPT-4V.
- Continuous enhancements through new model checkpoints and comprehensive guides for users and developers.
Comparative Analysis with SAM
Unlike SAM, which limits interaction types, SEEM offers robust interaction and semantic interpretation abilities. Its unified prompt encoder allows it to support more diverse use cases and custom prompts, potentially extending its functionality beyond existing models.
Conclusion
SEEM is a versatile, interactive tool that stands out in the field of image segmentation. Its ability to work with multiple prompts simultaneously makes it an invaluable asset for both simple and complex segmentation tasks. By utilizing a universal, multi-modal interface, SEEM sets a new standard for how users interact with and understand image content.