End-to-End Neural Network for Image Description
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Solution Overview
Problem
Current systems fail to generate accurate and comprehensive natural language descriptions of images, as they either lack the ability to describe object relationships and attributes or require multiple stages of processing, limiting their performance in image description tasks.
Innovation Solution
A fully trainable end-to-end system combining an encoder convolutional neural network and a decoder LSTM neural network to generate a sequence of natural language words that describe the contents of an image, including object relationships and attributes, using stochastic gradient descent for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If previous multi-stage processing systems are used for image description, then partial processing of image content can be achieved, but the systems fail to generate accurate and comprehensive natural language descriptions including object relationships and attributes
Solution Approach 1:
The patent merges the image processing and language generation functions into a single end-to-end neural network system. The convolutional neural network encoder and LSTM decoder are trained jointly to transform image data directly into natural language descriptions, eliminating the need for multiple separate processing stages while achieving comprehensive description of objects, relationships, and attributes
2Productivity
If end-to-end trainable systems are implemented, then better performance in image description tasks is achieved, but the system requires complex neural network architectures combining visual understanding models with language models
Solution Approach 1:
The patent creates a universal end-to-end system that performs multiple functions: visual feature extraction, semantic understanding, and natural language generation, all within a single trainable architecture. The system universally processes images to generate descriptions that include objects, relationships, and attributes without requiring separate specialized modules for each function
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating descriptions of input images. One of the methods includes obtaining an input image; processing the input image using a first neural network to generate an alternative representation for the input image; and processing the alternative representation for the input image using a second neural network to generate a sequence of a plurality of words in a target natural language that describes the input image.