Convolutional Self-Attention Transformer for Mobile Image Segmentation
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Solution Overview
Problem
Deep learning-based image segmentation methods require high computational resources and high-resolution inputs, making them unsuitable for mobile devices or scenarios with limited computational capacity, and scaling down these models leads to performance degradation.
Innovation Solution
Implement a convolutional self-attention transformer block and optional recursive atrous self-attention transformer blocks to generate image features, using a modified convolution procedure that outputs multiple values for different relative positions within a sliding window, and perform atrous self-attention operations based on dilation rates to enhance feature representation while maintaining computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based image segmentation methods are used, then segmentation accuracy is improved, but computational cost increases
Solution Approach 1:
The model architecture is segmented into encoder and decoder components with specific transformer blocks at different stages. The encoder uses CSA transformer blocks for local feature extraction and RASA blocks for global context, while the decoder progressively reconstructs segmentation maps. This modular segmentation allows efficient computation at each stage while maintaining overall accuracy.
Solution Approach 2:
Different transformer block types are applied at different spatial scales and network depths. CSA blocks with 3x3 kernels are used in early stages for local feature extraction, while RASA blocks with larger receptive fields are used in later stages for global context. This local quality differentiation optimizes computational resources according to the specific needs of each processing stage.
2Measurement precision
If deep learning-based image segmentation methods are used, then segmentation accuracy is improved, but device complexity increases
Solution Approach 1:
The complex model is divided into manageable transformer blocks (CSA and RASA) that can be independently implemented and optimized. Each block has a specific function (local vs global attention) and can be deployed on mobile devices with limited resources. This segmentation of complexity makes the overall system more manageable and deployable.
Solution Approach 2:
The model dynamically selects and applies different attention mechanisms (CSA vs RASA) based on the processing stage and feature requirements. This dynamic adaptation allows the system to balance accuracy and complexity in real-time, adjusting computational intensity according to the specific input and processing needs.
3Productivity
If conventional convolution is used, then computational efficiency is maintained, but feature representation capability is limited
Solution Approach 1:
The patent introduces self-attention mechanisms as an intermediary between conventional convolution operations. The CSA block acts as an intermediary that processes local features with attention weighting, while the RASA block serves as an intermediary for global context aggregation. These intermediary attention mechanisms enhance feature representation without completely replacing efficient convolution operations.
Solution Approach 2:
The model uses a composite architecture combining convolutional operations with transformer attention mechanisms. This composite approach integrates the computational efficiency of convolutions with the representational power of attention mechanisms, creating a hybrid system that leverages the strengths of both approaches.
4Adaptability or versatility
If model size is reduced for mobile deployment, then device compatibility is improved, but segmentation performance degrades
Solution Approach 1:
The model applies different levels of computational complexity to different parts of the network. Early stages use lighter CSA blocks for local feature extraction, while later stages use more capable RASA blocks for global context. This local quality differentiation allows the model to maintain performance while reducing overall size for mobile deployment.
Solution Approach 2:
The transformer blocks use parameter sharing and dilation rates to maintain representational capacity with fewer parameters. The RASA blocks employ atrous convolution with different dilation rates to expand receptive fields without increasing model size, enabling mobile deployment while preserving segmentation performance.
Data Source
AI summary
Systems and methods for image segmentation are described. Embodiments of the present disclosure receive an image depicting an object; generate image features for the image by performing a convolutional self-attention operation that outputs a plurality of attention-weighted values for a convolutional kernel applied at a position of a sliding window on the image; and generate label data that identifies the object based on the image features.