Multimodal Image Segmentation Using Response Heat Maps
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Solution Overview
Problem
Image segmentation under language indication faces challenges due to a semantic gap between images and linguistic descriptions, making it difficult to accurately segment specified objects.
Innovation Solution
Fusing visual features from an original image with text features from a description language to create a multimodal feature, determining a visual region using a response heat map, and employing an image segmentation model to refine the segmentation result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image segmentation is performed under language indication, then the ability to segment specified objects is improved, but the semantic gap between images and linguistic descriptions causes segmentation accuracy to deteriorate
Solution Approach 1:
The patent introduces a response heat map as an intermediary between the input image and the segmentation output. The heat map visually represents the model's attention distribution across different regions, serving as a mediator that bridges the semantic gap between language indications and image segmentation, thereby improving segmentation accuracy while maintaining language indication capability
Solution Approach 2:
The patent implements a feedback mechanism where the response heat map is generated based on the interaction between visual features and text features, and this heat map is then used to guide the final segmentation result. The feedback loop allows the system to adjust and refine segmentation based on the attention distribution, resolving the contradiction between adaptability and precision
2Loss of information
If visual features and text features are fused to create multimodal features, then the elimination of semantic gap is improved, but the complexity of the processing system increases
Solution Approach 1:
The patent merges visual features extracted from the image with text features from the language indication to create unified multimodal features. This combining approach allows the system to process both visual and linguistic information in an integrated manner, effectively eliminating the semantic gap while managing complexity through feature-level fusion rather than system-level complexity
3Measurement precision
If a response heat map is generated to determine visual regions, then the accuracy of target object identification is improved, but the computational time and resources increase
Solution Approach 1:
The patent generates the response heat map as a preliminary step before final segmentation, pre-processing the attention distribution information in advance. This preliminary action allows the subsequent segmentation to be more efficient and accurate, as the heat map already highlights the relevant regions, reducing the computational burden during the final segmentation phase
Data Source
AI summary
Provided are an image segmentation method and apparatus, a device, and a storage medium. The image segmentation method includes: fusing a visual feature corresponding to an original image with a text feature corresponding to a description language to obtain a multimodal feature, where the description language is used for specifying a target object to be segmented in the original image; determining a visual region of the target object according to an image corresponding to the multimodal feature and recording an image corresponding to the visual region as a response heat map; and determining a segmentation result of the target object according to the image corresponding to the multimodal feature and the response heat map.