Prompt Tuning Module for Intention-Aware Image Segmentation
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Solution Overview
Problem
The Segment Anything Model (SAM) often provides segmentation results that differ from a user's intention due to minute changes in user input position, as it fails to accurately estimate the user's intention for specific parts of an image.
Innovation Solution
An apparatus and method for image recognition that includes a first encoding module for extracting image feature information, a second encoding module for encoding user input for segmentation, a tuning module for modulating the prompt feature information based on the image and user input, and a decoding module for generating a segmentation mask. The tuning module learns representative points and tunes the prompt feature information to align with the user's intention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the SAM is trained with a very large amount of data to achieve general-purpose segmentation, then it can segment areas of an image without additional training, but it cannot accurately identify a user's intention to segment specific parts of an image
Solution Approach 1:
The patent segments the model into two distinct components: a foundation model trained on large-scale data for general-purpose segmentation, and a prompt tuning module specifically trained on user intention data. This segmentation allows each component to specialize in its respective function without compromising the other.
Solution Approach 2:
The patent performs preliminary training of the prompt tuning module with user intention data before actual use. This preliminary action equips the module with the ability to accurately interpret user intentions, which is then applied during inference to improve segmentation accuracy according to user intent.
2Measurement precision
If the user adjusts inputs multiple times to obtain a desired segmentation result, then the segmentation accuracy according to user intention can be improved, but the operation time and complexity increase
Solution Approach 1:
The patent incorporates a feedback mechanism where the prompt tuning module continuously learns from the difference between predicted and actual user intentions. This feedback loop enables the system to automatically adjust and improve its understanding of user intent without requiring multiple manual input adjustments.
Solution Approach 2:
The prompt tuning module performs self-adjustment by learning from training data and automatically adapting to user intentions. This self-service capability eliminates the need for users to manually adjust inputs multiple times, as the system autonomously optimizes its segmentation according to user intent.
3Adaptability or versatility
If the SAM provides segmentation results based on general use, then it can handle various types of images, but it cannot clearly estimate the user's intention for specific parts of the image
Solution Approach 1:
The patent introduces a prompt tuning module as an intermediary between the user's input and the foundation model's segmentation process. This intermediary component specifically processes and interprets user intention information, bridging the gap between general-purpose segmentation capability and specific user intent understanding.
Data Source
AI summary
Provided is an apparatus for image recognition, the apparatus including: a first encoding module configured to extract image feature information from an input image; a second encoding module configured to encode a user input related to segmentation of the input image to extract prompt feature information; a tuning module configured to tune the extracted prompt feature information according to a specified purpose using the image feature information and the extracted prompt feature information; and a decoding module configured to generate a segmentation mask based on the image feature information and the tuned prompt feature information.