Integrating Text and Image Pre-training for Multi-modal Algorithmic Reasoning

Zijian Zhang; Wei Liu

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arXiv · 2406.05318

Integrating Text and Image Pre-training for Multi-modal Algorithmic Reasoning

Zijian Zhang·Wei Liu

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Abstract

In this paper, we present our solution for SMART-101 Challenge of CVPR Multi-modal Algorithmic Reasoning Task 2024. Unlike traditional visual questions and answer tasks, this challenge evaluates abstraction, deduction and generalization ability of neural network in solving visuo-linguistic puzzles designed for specially children in the 6-8 age group. Our model is based on two pre-trained models, dedicated to extract features from text and image respectively. To integrate the features from different modalities, we employed a fusion layer with attention mechanism. We explored different text and image pre-trained models, and fine-tune the integrated classifier on the SMART-101 dataset. Experiment results show that under the data splitting style of puzzle split, our proposed integrated classifier achieves superior performance, verifying the effectiveness of multi-modal pre-trained representations.

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Zijian Zhang, Wei Liu. 2024-06-08. Integrating Text and Image Pre-training for Multi-modal Algorithmic Reasoning. https://arxiv.org/abs/2406.05318

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