Hybrid Vision Backbone With SSM Scan and Transformer Context
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Solution Overview
Problem
Transformer models are computationally expensive and inefficient for vision tasks due to quadratic complexity, while Mamba architectures are limited in capturing global context for spatial data processing.
Innovation Solution
A hybrid vision backbone architecture combining state space model (SSM)-based blocks and transformer blocks, with SSM blocks incorporating parallel selective scan operations for local and global understanding, and no positional embedding for transformer blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If transformer models are used for vision tasks, then global context representation is improved, but computational complexity increases quadratically
Solution Approach 1:
The patent divides the vision processing task into two segments: SSM blocks handle local spatial relationships with linear complexity, while transformer blocks handle global context with selective attention. This segmentation allows each component to specialize, reducing overall computational burden while maintaining global context capabilities.
Solution Approach 2:
Instead of applying full transformer attention to all token pairs (excessive action), the patent uses partial attention only where globally relevant interactions are needed. The SSM handles the bulk of local processing, allowing transformers to focus computation only on partial interactions that require global context.
2Productivity
If Mamba architecture is used for vision tasks, then computational efficiency is improved, but global context capture is limited
Solution Approach 1:
The patent merges SSM blocks and transformer blocks into a hybrid architecture where both components work together. The SSM provides efficient local processing while the transformer supplementally provides global context, combining the strengths of both architectures to overcome individual limitations.
Solution Approach 2:
The hybrid architecture serves multiple functions: the SSM handles local spatial relationships efficiently, while the transformer handles global context when needed. This multi-functionality allows the system to adapt computation to the specific requirements of different image regions, maintaining efficiency while capturing global context where necessary.
3Productivity
If autoregressive formulation is used in Mamba, then sequential processing efficiency is improved, but spatial relationship modeling is reduced
Solution Approach 1:
The patent applies different processing qualities to different spatial relationships: SSM blocks use autoregressive formulation for efficient local sequential processing, while transformer blocks provide full attention for modeling broader spatial relationships. This local quality differentiation allows efficient processing where applicable while maintaining adaptability where needed.
Data Source
AI summary
Neural network architectures for feature extraction from visual input. In at least one embodiment, a neural network architecture for a vision backbone includes hybrid stages with at least one state space model (SSM)-based block preceding at least one transformer block. In at least one embodiment, an SSM-based block includes parallel branches, one including an SSM and one without an SSM, and a concatenation layer for concatenating the output of each branch. In at least one embodiment, the SSM performs a parallel selective scan operation to efficiently map tokens of an input sequence to tokens of an output sequence via GPU acceleration.