Zero-Shot Visual Question Answering Using Relevant Patch Captions
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Solution Overview
Problem
Existing visual question answering (VQA) systems require significant adaptation and additional training, leading to computational overhead and limited scalability.
Innovation Solution
A zero-shot VQA framework that conjoins pre-trained vision-language models (PVLM) with network interpretability techniques to identify relevant image patches and generate informative captions, allowing for VQA tasks without additional training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-trained language models are adapted for vision modality with additional network components and training objectives, then VQA performance is improved, but device complexity and training requirements increase
Solution Approach 1:
The patent extracts the vision processing function from the language model by using a separate pre-trained vision-language model (PVLM) to generate captions, while the language model only processes the generated text. This separation eliminates the need for adapting the language model for vision, reducing complexity while maintaining performance.
Solution Approach 2:
The patent introduces an intermediary mechanism where the PVLM generates image captions that serve as bridges between the image input and the language model. This intermediary caption generation step allows the language model to focus solely on text processing without needing direct vision capabilities, thereby reducing overall system complexity.
2Adaptability or versatility
If additional network components and training objectives are added to adapt PLMs for vision, then VQA capability is enhanced, but ease of operation and scalability are reduced
Solution Approach 1:
The patent makes the language model universal by using it only for text processing tasks after the PVLM generates captions. The language model doesn't need to be specially adapted for vision, making it easier to operate and scale with existing language processing infrastructure while still achieving VQA capability through the combination of PVLM and language model.
3Reliability
If existing VQA systems perform additional training to adapt for specific tasks, then task-specific performance is improved, but loss of time and computational cost increase
Solution Approach 1:
The patent performs preliminary action by using a pre-trained vision-language model that has already been trained on extensive image-caption data. This pre-training eliminates the need for time-consuming task-specific training, as the PVLM can directly generate captions for new images without requiring re-training, thus reducing training time while maintaining task-specific performance through the caption generation process.
Data Source
AI summary
Embodiments described herein provide a zero-shot visual question answering (VQA) framework, which conjoins foundation network models with zero additional training. A first image and a question relating to the first image are received. The first image is divided into a plurality of image patches. A plurality of relevant image patches that are relevant to the question are determined, using a first neural network model, from the plurality of image patches. A plurality of image captions are generated, using a second neural network model, based on the plurality of relevant image patches. An answer to the question is generated based on the plurality of image captions.