Cross-Modality Alignment With Visual Embedding Highway for LLMs
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Solution Overview
Problem
Existing large language models (LLMs) face challenges in cross-modality alignment, particularly in integrating diverse input modalities such as text, image, and video, leading to significant visual information loss during processing.
Innovation Solution
A multi-modal interaction framework that aligns modality-specific encoders with LLM inputs and decoders using textual and visual alignment, incorporating a visual embedding highway (VEH) network to preserve low-level visual details, and employs a three-phase training scheme with custom-designed datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional LLM frameworks process visual data through text-only output, then the model can perform zero/few-shot learning tasks, but significant visual information is lost during processing
Solution Approach 1:
The patent introduces a visual embedding highway (VEH) network as an intermediary component that connects visual encoders to visual decoders. This VEH network serves as a dedicated channel to preserve and transmit visual features through the LLM processing pipeline, preventing the loss of visual information that occurs in conventional text-only output frameworks.
Solution Approach 2:
The patent segments the processing pipeline into distinct components: visual encoders, LLM, visual embedding highway, and visual decoders. By separating visual processing from text processing into independent modules connected by the VEH network, the system maintains visual information integrity while enabling cross-modality alignment capabilities.
2Adaptability or versatility
If the model integrates multiple modalities into a unified framework, then cross-modality understanding and generation improve, but the system complexity increases
Solution Approach 1:
The patent designs a universal framework where the LLM serves multiple functions: processing text inputs, generating text outputs, and coordinating visual processing through the VEH network. The visual embedding highway acts as a multi-functional bridge that enables both visual encoding and decoding operations, reducing the need for separate dedicated components for each modality.
Solution Approach 2:
The patent embeds the visual processing components (encoders and decoders) within the broader LLM framework structure. The VEH network is nested within the LLM architecture, allowing visual modalities to be processed through the existing text processing infrastructure without requiring entirely separate systems.
3Reliability
If visual features are passed through the LLM processing layers, then cross-modality alignment is achieved, but visual details are degraded
Solution Approach 1:
The visual embedding highway network serves as a specialized intermediary that preserves visual details while enabling alignment with LLM representations. The VEH network includes mechanisms to maintain high-resolution visual features throughout the processing pipeline, preventing the degradation that would otherwise occur when visual data passes through standard LLM layers.
Solution Approach 2:
The patent employs parameter changes in the VEH network to adapt visual features to different processing stages. By dynamically adjusting the representation parameters of visual embeddings as they traverse the VEH network, the system maintains both alignment accuracy with LLM space and preservation of fine-grained visual details.
Data Source
AI summary
Apparatuses, systems, and techniques for cross-modality alignment for large language models (LLMs), enabling enhanced multi-modal interaction. In at least one embodiment, a textual embedding is obtained by encoding a multi-modal input and algining the encoded results into a textual embedding space. A visual embedding is obtained based on features extracted from visual data in the multi-modal input using visual encoders. A multi-modal output is generated based on the textual embedding and the visual embedding.