Dynamic Double Space Tower
Weikai Sun, Shijie Song, Han Wang

TL;DR
This paper introduces a dynamic bidirectional spatial tower to improve reasoning and spatial relationship understanding in Visual Question Answering, achieving state-of-the-art results with fewer parameters.
Contribution
It proposes a novel spatial tower architecture replacing attention mechanisms to enhance spatial reasoning in VQA models.
Findings
The module improves spatial relationship processing across models.
The July VQA model with our method achieves state-of-the-art results.
Fewer parameters are needed for high performance.
Abstract
The Visual Question Answering (VQA) task requires the simultaneous understanding of image content and question semantics. However, existing methods often have difficulty handling complex reasoning scenarios due to insufficient cross-modal interaction and capturing the entity spatial relationships in the image.\cite{huang2023adaptive}\cite{liu2021comparing}\cite{guibas2021adaptive}\cite{zhang2022vsa}We studied a brand-new approach to replace the attention mechanism in order to enhance the reasoning ability of the model and its understanding of spatial relationships.Specifically, we propose a dynamic bidirectional spatial tower, which is divided into four layers to observe the image according to the principle of human gestalt vision. This naturally provides a powerful structural prior for the spatial organization between entities, enabling the model to no longer blindly search for…
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Taxonomy
TopicsStructural Analysis and Optimization
