Multimodal RNN for Novel Image Caption Generation
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Solution Overview
Problem
Existing methods for generating image captions lack the ability to create novel sentences describing images with novel combinations of objects and scenes, as they primarily focus on retrieval tasks rather than generating new descriptions.
Innovation Solution
A multimodal Recurrent Neural Network (m-RNN) model is developed, comprising a language model, a vision part using deep Convolutional Neural Networks, and a multimodal part that connects both, allowing for the generation of novel sentence descriptions and efficient image and sentence retrieval by learning a dense feature embedding and using a log-likelihood cost function for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If retrieval-based methods are used to generate image captions, then the system can efficiently retrieve existing captions from a database, but it lacks the ability to generate novel sentences describing images with new combinations of objects and scenes
Solution Approach 1:
The patent combines retrieval-based methods with generative capabilities by integrating a recurrent neural network that can both retrieve existing captions and generate novel sentences. The system merges the efficiency of database retrieval with the creativity of neural network generation to produce both existing and new captions.
Solution Approach 2:
The recurrent neural network is designed to perform multiple functions: it can retrieve existing captions from the database, generate novel sentences for images without exact matches, and adapt to different image types. This multi-functional approach allows the system to handle both retrieval and generation tasks within a single framework.
2Reliability
If a multimodal Recurrent Neural Network model is developed to generate novel sentences and perform retrieval, then the system achieves state-of-the-art performance in both tasks, but the model complexity increases significantly
Solution Approach 1:
The multimodal RNN model is segmented into distinct functional components: a language model module for sentence generation, a vision module for image feature extraction, and a multimodal integration module that connects both. This segmentation allows each component to specialize in specific tasks while working together to achieve high accuracy.
Solution Approach 2:
The patent introduces a multimodal embedding layer as an intermediary that bridges the language model and vision model. This intermediary converts both text and image inputs into a common feature space, enabling effective integration without requiring direct complex interactions between the language and vision components.
3Ease of operation
If dense feature embedding is learned for both images and sentences to enable retrieval, then the system can efficiently map features to the same semantic space, but the training computational requirements increase
Solution Approach 1:
The system performs preliminary action by pre-training the multimodal RNN on large datasets to learn dense feature embeddings before deployment. This pre-training phase establishes the semantic space mapping in advance, allowing efficient retrieval operations during actual use without requiring intensive computational resources at runtime.
Data Source
AI summary
Presented herein are embodiments of a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. In embodiments, it directly models the probability distribution of generating a word given a previous word or words and an image, and image captions are generated according to this distribution. In embodiments, the model comprises two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. In embodiments, these two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of an embodiment of model was validated on four benchmark datasets, and it outperformed the state-of-the-art methods. In embodiments, the m-RNN model may also be applied to retrieval tasks for retrieving images or captions.