m-RNN Model for Novel Visual Concept Learning
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Solution Overview
Problem
Current computer vision methods focus on learning classifiers for single words and images, failing to efficiently learn novel visual concepts from sentence descriptions, which is a crucial task for providing intuitive interfaces and services, especially when data is limited.
Innovation Solution
The proposed solution involves adapting a Multimodal Recurrent Neural Networks (m-RNN) model with transposed weight sharing and baseline probability fixation to learn novel visual concepts from a few sentence descriptions, allowing the model to expand its dictionary without extensive retraining and maintaining accuracy on original concepts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the model is retrained on all data to learn novel concepts, then the learning accuracy improves, but the training time and computational cost increase significantly
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) pretraining on large-scale image-text pairs to learn general visual concepts, and (2) fine-tuning on novel concept sentences with limited data. This segmentation allows the model to efficiently adapt to new concepts without retraining on all data, resolving the contradiction between learning accuracy and training time.
Solution Approach 2:
The model performs preliminary learning of general visual concepts through pretraining on large-scale data before encountering novel concepts. This preliminary action equips the model with transferable knowledge that accelerates subsequent learning of novel concepts from limited sentences, avoiding the need for extensive retraining.
2Productivity
If the model learns from limited novel concept sentences, then the training efficiency improves, but the learning accuracy deteriorates due to overfitting
Solution Approach 1:
The model performs preliminary learning of general visual concepts through pretraining on large-scale data before encountering novel concepts. This preliminary action equips the model with transferable knowledge that accelerates subsequent learning of novel concepts from limited sentences, avoiding the need for extensive retraining.
Solution Approach 2:
The patent employs parameter freezing during the fine-tuning phase, where previously learned parameters from pretraining are kept fixed while only novel concept-related parameters are updated. This parameter change strategy prevents overfitting to limited novel concept sentences while maintaining training efficiency.
3Adaptability or versatility
If the model expands its dictionary to include novel concepts, then the adaptability improves, but the complexity of the model increases
Solution Approach 1:
The patent implements a universal visual-language representation framework where the same neural network architecture and parameters serve both general visual concept understanding and novel concept learning. This universality allows dictionary expansion to novel concepts without increasing model complexity, as the model leverages shared representations across different concept types.
Data Source
AI summary
Described herein are systems and methods that address the task of learning novel visual concepts, and their interactions with other concepts, from a few images with sentence descriptions. Using linguistic context and visual features, embodiments are able to efficiently hypothesize the semantic meaning of new words and add them to model word dictionaries so that they can be used to describe images which contain these novel concepts. In the experiments, it was shown that the tested embodiments effectively learned novel visual concepts from a few examples without disturbing the previously learned concepts.