Eidetic Memory Blocks for High-Fidelity Vision Segmentation
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Solution Overview
Problem
Current methods for interactive object segmentation in computer vision, such as those using convolutional neural networks, face challenges in achieving high-fidelity and reproducible results due to the complexity of the task and reliance on hand-crafted features and distance metrics.
Innovation Solution
The introduction of eidetic memory blocks (EMBs) into deep neural networks, which combine low-level pre-trained features, edge-enhanced features from wavelet transformations, and user-enabled domain-persistent features, enhances network efficiency and inference quality for tasks like pixel-level segmentation by reducing the need for extra resources to encode domain-persistent features and allowing for improved feature propagation through skip connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional convolutional neural networks are used for interactive object segmentation, then the network can process images, but the segmentation accuracy and fidelity are insufficient due to reliance on hand-crafted features
Solution Approach 1:
The patent segments the feature extraction process into distinct modules: hand-crafted feature extraction module, deep learning feature extraction module, and fusion module. This allows each module to specialize in specific tasks while maintaining overall system accuracy without excessive complexity
Solution Approach 2:
The patent combines hand-crafted features (distance metrics, gradient directions) with deep learning features through a fusion module. This merging approach leverages the strengths of both conventional methods and modern neural networks to achieve high-fidelity segmentation
2Reliability
If hand-crafted features and distance metrics are used for interactive segmentation, then the implementation is simpler, but the reproducibility and high-fidelity results are compromised
Solution Approach 1:
The patent introduces an intermediary fusion module that bridges hand-crafted features and deep learning features. This mediator combines the simplicity of conventional features with the robustness of learned features, improving reproducibility while maintaining implementation feasibility
Solution Approach 2:
The patent dynamically adjusts the weighting and contribution of different feature types (hand-crafted vs. deep learning) based on task requirements and input characteristics, allowing the system to adapt between simplicity and reliability depending on the specific application
3Measurement precision
If deep neural networks with multiple layers are used to improve segmentation quality, then the inference quality improves, but the computational resources required increase significantly
Solution Approach 1:
The patent applies deep learning features selectively rather than throughout the entire network. Hand-crafted features handle straightforward cases while deep learning features are activated for complex regions, reducing overall computational resource consumption while maintaining high inference quality where needed
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The use of EMBs in convolutional neural networks improves model efficiency and segmentation accuracy, addressing the high-fidelity problem of interactive object segmentation and enabling applications in tasks like rotoscoping and medical imaging with increased reproducibility and precision.
Implementation Method 1
edge-enhanced features from wavelet transformations
Data Source
AI summary
Methods, systems, and apparatus for high-fidelity vision tasks using deep neural networks are disclosed. An example apparatus includes a feature extractor to extract low-level features and edge-enhanced features of an input image processed using a convolutional neural network, an eidetic memory block generator to generate an eidetic memory block using the extracted low-level features or the extracted edge-enhanced features, and an interactive segmentation network to perform image segmentation using the eidetic memory block, the eidetic memory block used to propagate domain-persistent features through the segmentation network.