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

• URL: http://arxiv.org/abs/2406.05318v1
• Date: Sat, 8 Jun 2024 01:45:06 GMT
• Title: Integrating Text and Image Pre-training for Multi-modal Algorithmic Reasoning
• Authors: Zijian Zhang, Wei Liu,
• Abstract summary: 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. Our model is based on two pre-trained models, dedicated to extract features from text and image respectively.
• Score: 7.84845040922464
• License: http://creativecommons.org/licenses/by/4.0/
• 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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