Enhanced multi-modal large language model for referring expression segmentation
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Solution Overview
Problem
Multi-modal Large Language Models (MLLMs) struggle to accurately label images corresponding to specific objects mentioned in natural language expressions due to a lack of training in understanding pixel-level details, leading to inaccuracies in referring expression segmentation (RES).
Innovation Solution
The Segment Anything Model (SAM) is integrated with MLLMs to enhance object localization at the pixel level without altering the LLM architecture, using pre-trained convolutional neural networks and attention mechanisms to generate high-quality semantic-free segmentation masks based on prompts, and employs low rank adaptation to align MLLMs with segmentation models for improved RES.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If MLLMs are used for referring expression segmentation, then language understanding capability is improved, but pixel-level segmentation precision deteriorates
Solution Approach 1:
The patent merges MLLM with SAM (Segment Anything Model) to create an integrated system that combines language understanding capabilities with pixel-level segmentation precision. The MLLM processes natural language prompts while SAM handles the actual pixel-level segmentation, allowing the system to achieve both language comprehension and accurate segmentation simultaneously.
Solution Approach 2:
The patent introduces an intermediary module that translates MLLM's language understanding output into prompts suitable for SAM. This intermediary component bridges the gap between the language model's conceptual understanding and the segmentation model's pixel-level processing, enabling effective coordination between the two models.
2Measurement precision
If SAM is integrated with MLLM, then segmentation precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the overall system into distinct functional modules: MLLM for language understanding, an intermediary translation module, and SAM for segmentation. This modular segmentation allows each component to specialize in its specific task while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The patent leverages SAM's universal segmentation capability that can handle various types of segmentation tasks across different domains. By using a pre-trained SAM model, the system achieves high segmentation precision without requiring task-specific fine-tuning, thereby reducing the overall system complexity.
3Reliability
If low rank adaptation is applied, then model alignment is improved, but computational resources increase
Solution Approach 1:
The patent applies low rank adaptation by modifying only a small subset of parameters in the MLLM rather than fine-tuning the entire model. This parameter-efficient approach uses low-rank decomposition to update only the necessary components, achieving effective model alignment while significantly reducing computational resource requirements compared to full fine-tuning.
Data Source
AI summary
Processors, systems, and techniques to identify one or more regions of an image, using at least one neural network based at least in part on a description of the region(s) of the image. In at least one embodiment, a natural language expression including a description is input into at least one neural network, and the at least one neural network identifies a portion of pixels in an image corresponding to the description. In at least one embodiment, one or more neural networks use semantic information obtained from language data describing at least one region of interest in an image to generate classifications classifying a plurality of locations in the image as being inside or outside the at least one region, and use the plurality of locations to generate a segmentation mask. In at least one embodiment, the segmentation mask is used to cause a machine to perform task(s).