Neural Network Attention Mechanism for Content Localization
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Solution Overview
Problem
Current methods for localizing and classifying content in data sets, such as video, text, or audio files, are inefficient, requiring extensive manual review and lacking in automation for identifying key features or noteworthy content.
Innovation Solution
A system utilizing a neural network, comprising a recurrent neural network (RNN) and a feedforward neural network (NN), applies an attention vector to generate weighted activations, which are then used to classify and prioritize content within a data set, allowing for automated identification of key features and reduced review time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review methods are used to localize and classify content in data sets, then review accuracy can be maintained, but the time and effort required for review increases significantly
Solution Approach 1:
The patent introduces an attention mechanism as an intermediary between the RNN processing and final classification. This attention mechanism automatically identifies and weights important portions of the data set, acting as a mediator that guides the review process to focus on critical content while filtering out less important portions, thereby reducing review time without sacrificing accuracy
Solution Approach 2:
The patent replaces manual mechanical review processes with an automated neural network system comprising RNN and attention mechanisms. This substitution transforms the review process from a labor-intensive manual operation to an automated computational process that can rapidly analyze and classify content while maintaining or improving identification accuracy
2Productivity
If automated neural network methods are used to localize and classify content, then review time is reduced, but system complexity increases
Solution Approach 1:
The patent segments the complex processing task into distinct functional components: an RNN layer for sequential processing, an attention mechanism for selective weighting, and a classification layer for final categorization. This segmentation allows each component to specialize in a specific function, improving overall processing efficiency while making the complex system more manageable and interpretable
Solution Approach 2:
The attention mechanism serves as an intermediary layer that simplifies the overall system architecture by providing a clear interface between the RNN's sequential processing output and the classification requirements. It translates complex sequential data into weighted representations that are easier for the classification layer to process, thereby reducing the effective complexity of the system
3Measurement precision
If attention mechanisms are applied to generate weighted activations, then key feature identification is improved, but computational requirements increase
Solution Approach 1:
The attention mechanism applies local quality by generating different weight values for different portions of the input data based on their importance. Instead of uniformly processing all data points with equal computational resources, the system concentrates computational effort on high-attention regions while reducing processing for low-attention regions, thereby improving feature identification precision while optimizing energy usage
Data Source
AI summary
Disclosed herein includes a system, a method, and a device for localizing and classifying content in a data set. A device can provide a sequence of portions of a data set to a neural network to generate a plurality of activations. Each activation of the plurality of activations can include at least one value from a layer of the neural network. The device can apply an attention vector to each activation of the plurality of activations to generate a sequence of values. A normalization function can be applied to the sequence of values to generate a sequence of attention scores according to the sequence of values. The device can identify or localize one or more portions in the sequence of portions of the data based in part on the sequence of attention scores.