Dual-Attention Vision Transformer for Small-Data Image Segmentation
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Solution Overview
Problem
Existing transformers for image segmentation require large datasets for effective training, making them incompatible with small datasets, and lack strong inductive biases.
Innovation Solution
Implementing a dual-attention vision transformer that utilizes inter-attention and intra-attention mechanisms to enhance the training process, incorporating user feedback in the form of positive and negative clicks to reduce the data requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vision transformers are used for interactive image segmentation, then segmentation accuracy is improved, but large datasets are required which increases training complexity and time
Solution Approach 1:
The patent segments the image into multiple patches and processes them through separate transformer encoders, then combines results through attention mechanisms. This allows the model to learn from smaller local regions rather than requiring large-scale global context, reducing training data requirements while maintaining segmentation accuracy.
Solution Approach 2:
The patent introduces an intermediary segmentation model that generates preliminary segmentation results, which are then used to guide the vision transformer's attention. This intermediary component helps the model focus on relevant regions with fewer training examples, bridging the gap between limited data and high accuracy requirements.
2Manufacturing precision
If vision transformers are used for interactive image segmentation, then segmentation quality is improved, but training time and computational resources increase
Solution Approach 1:
By dividing the image into patches and processing them through multiple encoder layers in parallel, the training process becomes more efficient. Each encoder layer processes specific depth ranges independently, allowing for faster convergence while maintaining high segmentation quality through progressive feature refinement.
Solution Approach 2:
The patent implements dynamic attention mechanisms that adaptively adjust which regions receive more computational focus during training. This dynamic resource allocation allows the model to achieve high segmentation quality by concentrating computational power on critical regions rather than uniformly processing all pixels, thereby reducing overall training time.
3Adaptability or versatility
If standard vision transformers are used, then they can process images, but they lack strong inductive biases making them inefficient for interactive tasks
Solution Approach 1:
The patent incorporates local quality constraints by processing image patches through multiple encoder layers that progressively refine local features before global attention aggregation. This local-to-global processing hierarchy introduces inductive biases that guide the model to respect local image structures and boundaries, improving adaptability to interactive segmentation tasks while maintaining training efficiency.
Solution Approach 2:
The patent performs preliminary feature extraction and patch embedding before the main attention processing stages. This preliminary action pre-processes the input data into a format that already contains useful structural information, reducing the computational burden during subsequent training stages and improving overall training efficiency while maintaining task adaptability.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to implement dual attention vision transformers for interactive image segmentation. In some examples, the apparatus includes memory, instructions, and processor circuitry to execute and/or instantiate the instructions to partition information in an input tensor into a plurality of tensor crops, the input tensor representing image data of an image. The processor circuitry is further to execute and/or instantiate the instructions to create a dual-attention tensor representation of the input tensor, including an inter-attention tensor representation describing a first correlation between the plurality of tensor crops and an intra-attention tensor representation describing a second correlation between a plurality of layers in a tensor crop of the plurality of tensor crops.