One-stage Dichotomous Segmentation via Multi-scale Attention
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Solution Overview
Problem
Existing dichotomous segmentation methods often miss important details or require lengthy processing times due to their multi-stage processes when attempting to recognize foreground objects in high-resolution images.
Innovation Solution
A one-stage learning model based on an encoder-decoder architecture with a CNN-based feature extractor using multi-scale convolutional attention blocks and hamburger heads for efficient feature extraction and prediction, directly processing high-resolution images without the need for low-resolution inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-stage segmentation processes are used to recognize foreground objects in high-resolution images, then segmentation accuracy can be improved, but processing time increases significantly
Solution Approach 1:
The model segments the feature extraction and prediction process into distinct functional blocks (convolutional attention blocks, hamburger heads) that operate in parallel within a single stage, eliminating sequential multi-stage processing while maintaining accuracy
Solution Approach 2:
The encoder pre-extracts comprehensive features from the entire high-resolution image in advance, and the decoder immediately generates segmentation predictions without intermediate processing stages, reducing overall processing time
2Adaptability or versatility
If transformers are used as feature extractors with self-attention mechanisms, then learning capability is improved, but computation complexity becomes too high for practical high-resolution image processing
Solution Approach 1:
The patent replaces the transformer's self-attention mechanism with a CNN-based convolutional attention mechanism that uses local convolutional operations instead of global attention computations, significantly reducing computational complexity while maintaining effective feature learning
Solution Approach 2:
The convolutional attention blocks apply attention mechanisms locally through depth-wise convolutions with different receptive fields, capturing multi-scale context efficiently without the quadratic computational complexity of global self-attention
3Productivity
If a one-stage model directly processes high-resolution images, then processing speed is improved, but parameter requirements and memory usage increase
Solution Approach 1:
The model segments the processing task into encoder and decoder components with specialized functions, allowing efficient parameter utilization while maintaining high-resolution processing capability
Solution Approach 2:
The hamburger head architecture dynamically adjusts parameter usage through matrix decomposition and non-linear transformations, enabling the model to process high-resolution images with controlled parameter requirements
Data Source
AI summary
Provided is a method and apparatus for obtaining a foreground image from an input image containing the foreground object in a scene. Embodiments use multi-scale convolutional attention values, one or more hamburger heads and one or more multilayer perceptrons to obtain a segmentation map of the input image. In some embodiments, progressive segmentation is applied to obtain the segmentation map.