Summary of Vsa4vqa: Scaling a Vector Symbolic Architecture to Visual Question Answering on Natural Images, by Anna Penzkofer et al.
VSA4VQA: Scaling a Vector Symbolic Architecture to Visual Question Answering on Natural Images
by Anna Penzkofer, Lei Shi, Andreas Bulling
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed VSA4VQA is a novel 4D implementation of Vector Symbolic Architectures (VSAs) designed for the challenging task of Visual Question Answering (VQA). By scaling VSAs to complex spatial queries, VSA4VQA achieves competitive performance to state-of-the-art deep learning methods. The model utilizes the Semantic Pointer Architecture (SPA) to encode objects in a hyperdimensional vector space and extends SPA to include dimensions for object’s width and height. Additionally, learned spatial query masks are introduced to perform spatial queries, integrating a pre-trained vision-language model for answering attribute-related questions. The VSA4VQA method is evaluated on the GQA benchmark dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to use Vector Symbolic Architectures (VSAs) that can understand complex images and answer questions about them. This is called VSA4VQA. It’s special because it can work with real-life images, not just simple ones. The model uses something called the Semantic Pointer Architecture (SPA) to understand objects in pictures and adds extra information about object size and position. To make decisions, it uses a combination of learned patterns and a pre-trained language model. The team tested VSA4VQA on a difficult dataset and showed that it can do just as well as other state-of-the-art methods. |
Keywords
» Artificial intelligence » Deep learning » Language model » Question answering » Vector space
