Dynamic CNN Hyperparameter Adjustment for First-Person Vision
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Solution Overview
Problem
Conventional convolutional neural networks (CNNs) have fixed hyperparameters, which are inadequate for dynamically adjusting to moving objects and subjects, leading to inefficient image recognition in dynamic environments, such as those captured from mobile devices with changing vantage points.
Innovation Solution
The method involves training a neural network to determine dynamic padding and stride values by averaging outputs from a deep neural network and multiple regression models, allowing the CNN to adjust its feature extraction and focus based on input image feature vectors, thereby improving first-person computer vision interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed hyperparameters are used in CNN, then the structure is simple and easy to implement, but the system cannot dynamically adjust to moving objects and changing vantage points
Solution Approach 1:
The patent applies dynamics by transforming fixed hyperparameters into dynamic values that adapt to input images. Multiple regression models and deep neural networks compute padding and stride values based on image features, enabling the CNN to adjust its structure dynamically according to the content and scale of objects in the image, thus resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The patent changes the parameters (padding and stride values) from fixed constants to computed variables. By using regression models and neural networks to determine these parameters based on image descriptors, the system achieves dynamic adaptability while managing complexity through automated parameter optimization rather than manual tuning.
2Measurement precision
If dynamic hyperparameter adjustment is implemented, then image recognition accuracy for moving objects improves, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by training multiple regression models and deep neural networks in advance on labeled datasets. These pre-trained models store learned relationships between image features and optimal hyperparameters, allowing the system to quickly compute padding and stride values during inference without performing complex training computations in real-time, thus improving accuracy while managing computational load.
Solution Approach 2:
The patent introduces intermediary models (multiple regression models and deep neural networks) that act as mediators between the input image and the CNN hyperparameters. These intermediaries process image descriptors and output optimized padding and stride values, enabling accurate dynamic adjustment without requiring the main CNN to handle the computational complexity of hyperparameter optimization.
3Measurement precision
If multiple models are used to determine hyperparameters, then the accuracy of padding and stride determination improves, but the system complexity and training requirements increase
Solution Approach 1:
The patent merges multiple approaches (multiple regression models and deep neural networks) into a unified hyperparameter determination system. The regression models provide baseline predictions while the neural network captures complex non-linear relationships, and their outputs are combined to determine final padding and stride values. This merging achieves high accuracy by leveraging complementary strengths of different model types.
Solution Approach 2:
The patent creates a universal hyperparameter determination system that can handle various image types and scenarios using the same set of models. The regression models and neural network are trained on diverse datasets and can generalize to different objects, scales, and conditions, providing accurate hyperparameter recommendations across multiple applications without requiring separate specialized models for each case.
Data Source
AI summary
Methods and systems for visually focused first-person neural network interpretation are disclosed. A method includes: receiving, by a computing device, an image; determining, by the computing device, feature vectors from the image; determining, by the computing device, a first padding value and a first stride value by inputting the feature vectors into a deep neural network; determining, by the computing device, a second padding value and a second stride value by inputting the feature vectors into at least one multiple regression model; determining, by the computing device, padding by averaging the first padding value and the second padding value; determining, by the computing device, stride by averaging the first stride value and the second stride value; and classifying, by the computing device, the image using a convolutional neural network using the padding and the stride.