Frozen Language Model Multimodal Few-Shot Learning
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Solution Overview
Problem
Current neural network systems face challenges in effectively performing multi-modal tasks, such as image captioning and visual question answering, as they require training on diverse input sequences of images and text, which is complex and resource-intensive, especially when the language model is not fine-tuned for image processing.
Innovation Solution
A system combining a pre-trained language model neural network with an image encoder neural network, where the image encoder generates image embeddings that can be processed by the language model to produce text responses, allowing for multi-modal tasks without fine-tuning the language model, leveraging the few-shot learning ability of Transformer-based models to perform a wide range of tasks with interleaved image and text embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a language model is fine-tuned for image processing tasks, then task performance improves, but training complexity and resource requirements increase
Solution Approach 1:
The system divides the processing pipeline into two independent components: an image encoder that extracts visual features and a pre-trained language model that generates text responses. This segmentation allows each component to specialize without requiring the language model to be fine-tuned on image data, reducing training complexity while maintaining task performance.
Solution Approach 2:
The pre-trained language model serves multiple functions by processing both text inputs and image-encoded inputs without requiring separate fine-tuning for different modalities. This universal approach allows the same model to handle various multi-modal tasks (image captioning, visual question answering, etc.) while retaining its original language capabilities.
2Adaptability or versatility
If diverse training data is used for multi-modal tasks, then model versatility improves, but training resources and time increase
Solution Approach 1:
The language model is pre-trained on extensive text corpora before being deployed for multi-modal tasks. This preliminary action equips the model with general language understanding and generation capabilities, reducing the need for additional training when adapting to specific image-related tasks and enabling faster deployment.
Solution Approach 2:
The image encoder acts as an intermediary that translates visual information into a format compatible with the pre-trained language model. This intermediary component enables the model to process diverse image inputs without requiring the language model itself to be retrained, preserving versatility while minimizing training time.
3Productivity
If the language model is frozen without fine-tuning, then training efficiency improves, but task adaptation capability may worsen
Solution Approach 1:
The system changes the parameter representation of images by encoding them into embedding vectors that match the dimensionality and structure of text embeddings. This parameter transformation allows the frozen language model to process image inputs effectively without fine-tuning, maintaining both training efficiency and task adaptation capability through proper feature alignment.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing multi-modal inputs using language models. In particular, the inputs include an image, and the image is encoded by an image encoder neural network to generate a sequence of image embeddings representing the image. The sequence of image embeddings is provided as at least part of an input sequence to that is processed by a language model neural network.