INT Improves Promptable Image Segmentation by Refining Information Selection

New Method for Promptable Segmentation: INT Optimizes the Selection of Relevant Information

Promptable image segmentation has made significant progress in recent years. The goal is to segment different objects within an image based on a single task description – the so-called prompt. This allows for flexible and efficient image analysis without the need to train a specific model for each new task. A promising approach leverages the capabilities of Vision-Language Models (VLMs) to derive instance-specific prompts from a general, task-agnostic prompt, which then control the segmentation process.

However, one challenge is that VLMs can struggle to correctly generate these instance-specific prompts for certain image instances. This leads to inaccurate segmentation results. A new method called INT (Instance-specific Negative Mining for Task-Generic Promptable Segmentation) addresses this problem by optimizing the selection and processing of relevant information in the prompt generation process.

INT: Highlighting Relevant Information, Hiding Irrelevant Information

The core of INT lies in the adaptive reduction of the influence of irrelevant (negative) prior knowledge and the simultaneous amplification of the most relevant information. Through a process of "Negative Mining," the most contrasting and thus most informative information is selected to optimize the generation of instance-specific prompts. INT consists of two main components:

The first component is the generation of instance-specific prompts. In an iterative process, inaccurate information is filtered out to improve the quality of the prompt. The second component is the generation of semantic masks. This component ensures that the segmentation of each image instance correctly matches the semantics of the instance-specific prompts.

Promising Results in Various Applications

The effectiveness of INT was evaluated using six different datasets, including images with camouflaged objects and medical images. The results show that INT achieves improved segmentation accuracy compared to existing methods. The method proves to be robust and scalable and can thus be used for a variety of applications.

The ability to work with general prompts opens up new possibilities for image analysis. INT helps to overcome the challenges in generating instance-specific prompts and improves the accuracy of promptable segmentation. This could lead to further advancements in areas such as medical imaging, robotics, and automated image analysis.

Future Research

Further research could focus on optimizing the Negative Mining process to further increase the efficiency and accuracy of INT. The application of INT to further datasets and application areas is also a promising research approach.

Bibliography: - Hu, J., Cheng, Z., & Gong, S. (2025). INT: Instance-Specific Negative Mining for Task-Generic Promptable Segmentation. arXiv preprint arXiv:2501.18753. - Hugging Face. (n.d.). Retrieved from [Hugging Face Website URL] - Papers. (n.d.). Retrieved from [Papers Website URL]