Adversarial Dialogue Model for Visual Interaction
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Solution Overview
Problem
Current methods for training artificial intelligence models in visual dialogue systems face challenges such as high computational consumption, slow convergence rates, and limited ability to integrate multi-modal information effectively, particularly due to the lack of interaction with the physical world.
Innovation Solution
An autonomous evolution intelligent dialogue method is introduced, utilizing a dialogue model comprising an image encoding model based on convolutional neural networks and text encoding models using recurrent neural networks, with adversarial training and supervised learning to optimize the model's performance, allowing it to interact with the physical environment and generate more accurate and fluent responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning is used to simulate interactions with the physical world, then the model can perform autonomous updating, but the action space and reward mechanisms must be redefined for different tasks, resulting in poor universality and complex design
Solution Approach 1:
The patent introduces a universal discriminator model that can evaluate state vectors from different tasks without requiring task-specific redefinition. The discriminator learns to distinguish real physical world states from generated states through adversarial training, providing a task-agnostic evaluation mechanism that enhances universality across different interaction scenarios
2Manufacturing precision
If generative adversarial learning and reinforcement learning are used to improve visual dialogue quality, then dialogue quality improves to a certain extent, but computational consumption becomes excessive and convergence rate slows down
Solution Approach 1:
The patent extracts the physical world interaction evaluation function from the complex reinforcement learning framework into a separate discriminator model. This discriminator independently evaluates state vectors using adversarial learning, separating the quality assessment function from the dialogue generation process, thereby reducing computational burden while maintaining dialogue quality
Solution Approach 2:
The discriminator is pre-trained to recognize characteristics of real physical world states before being used to evaluate dialogue states. This preliminary training allows the discriminator to provide effective feedback signals without requiring extensive computational resources during the main training process, improving convergence rate
3Measurement precision
If current supervised training methods are used, then the model can understand data through statistical distribution, but data interpretation deficiencies occur when important parts lack labels, resulting in dramatic decline in effective supervised learning
Solution Approach 1:
The patent implements a feedback mechanism where the discriminator evaluates generated state vectors and provides gradient signals back to the dialogue model. This feedback loop allows the model to learn from the evaluation results and improve its understanding of physical world interactions, compensating for the lack of labeled data in important regions
Solution Approach 2:
The discriminator serves as an intermediary between the dialogue model and the physical world semantics. It translates the evaluation of state vectors into meaningful feedback that guides the dialogue model's learning, enabling the model to interpret unlabelled data more effectively through the discriminator's learned understanding of physical world states
Data Source
AI summary
The method of the present disclosure includes: obtaining an image to be processed and a question text corresponding to the image; using an optimized dialogue model to encode the image into an image vector and encode the question text into a question vector; generating a state vector based on the image vector and the question vector; decoding the state vector to obtain and output an answer text. A discriminator needs to be introduced in an optimization process of the optimized dialogue model. The dialogue model and the discriminator are alternately optimized until a value of a hybrid loss function of the dialogue model and a value of a loss function of the discriminator do not decrease or fall below a preset value, thereby accomplishing the optimization process.