How does the optimal number of modality-specific experts in SMoES scale with VLM backbone size (e.g., 7B vs 13

Abstract

Abstract: There has been a rapid progress in the task of Visual Question Answering with improved model architectures. Unfortunately, these models are usually computationally intensive due to their sheer size which poses a serious challenge for deployment. We aim to tackle this issue for the specific task of Visual Question Answering (VQA). A Convolutional Neural Network (CNN) is an integral part of the visual processing pipeline of a VQA model (assuming the CNN is trained along with entire VQA model). In this project, we propose an efficient and modular neural architecture for the VQA task with focus on

Research Question

How does the optimal number of modality-specific experts in SMoES scale with VLM backbone size (e.g., 7B vs 13B vs 34B) for ChartQA accuracy and throughput under distribution shift to unseen chart types?

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