Multimodal Model Segmentation Codebook Generation
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Solution Overview
Problem
Current large-scale multimodal models (LMMs) face limitations in processing multimodal input, particularly in region-specific comprehension tasks and handling multiple target objects in real-world scenarios, due to reliance on pre-trained image segmentation models and limited prompt encoder capabilities.
Innovation Solution
A multimodal data processing method that utilizes a multimodal model to process target questions and images, generating a text portion and segmentation codebooks indicating feature information of objects at multiple scales. These codebooks are then decoded using an image decoder model to produce segmentation masks, which are used to determine answers to the target questions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-trained image segmentation models are used for region-specific comprehension tasks, then segmentation accuracy is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent combines the image segmentation capability directly into the multimodal model by having the visual encoder output segmentation codebooks that are integrated with the language model processing. This merging eliminates the need for separate pre-trained segmentation models while maintaining segmentation functionality within the unified multimodal architecture.
Solution Approach 2:
The visual encoder in the multimodal model performs multiple functions simultaneously: it extracts visual features for understanding and generates segmentation codebooks for region-specific comprehension. This multi-functionality allows the same component to serve both global image understanding and localized object segmentation without requiring additional specialized models.
2Adaptability or versatility
If additional segmentation models are integrated into multimodal models, then multi-objective inference segmentation capability is improved, but device complexity increases
Solution Approach 1:
The patent merges segmentation functionality directly into the multimodal model's visual encoder, which generates segmentation codebooks that are processed alongside language inputs. This integration allows the model to handle multiple objectives (global understanding and localized segmentation) within a single unified architecture rather than combining separate models.
Solution Approach 2:
The visual encoder segments the input image into multiple scales and generates corresponding segmentation codebooks that represent different regions and objects. This segmentation capability is embedded within the multimodal model itself, enabling multi-objective inference without requiring external segmentation models.
3Measurement precision
If pre-trained segmentation models are used, then object region detection is improved, but processing time and computational resources increase
Solution Approach 1:
The patent combines segmentation and language processing into a single multimodal model that processes both visual and textual inputs simultaneously. By merging these functions, the model eliminates the sequential processing steps required when using separate pre-trained segmentation models, thereby reducing overall processing time while maintaining detection accuracy.
Solution Approach 2:
The visual encoder pre-processes the input image to generate multi-scale segmentation codebooks that capture object regions at different levels of detail. This preliminary segmentation action is performed as part of the initial visual feature extraction, preparing the data for subsequent language-model-based reasoning without requiring additional separate processing stages.
Data Source
AI summary
Embodiments of the disclosure provide a method, an apparatus, a device and a medium for multimodal data processing. The method includes: obtaining a target question and a target image associated with the target question; processing the target question and the target image by using a multimodal model to obtain an output of the multimodal model, the output including a text portion and at least one segmentation codebook for the target image indicating feature information of at least one object related to the target question; decoding the at least one segmentation codebook based on the target image by using an image decoder model to obtain at least one segmentation mask, the at least one segmentation mask indicating a region where the at least one object is located in the target image; and determining an answer to the target question based on the text portion and the at least one segmentation mask.