Integrating Text and Image Pre-training for Multi-modal Algorithmic Reasoning
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913381943869440 |
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| author | Zhang, Zijian Liu, Wei |
| author_facet | Zhang, Zijian Liu, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05318 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrating Text and Image Pre-training for Multi-modal Algorithmic Reasoning Zhang, Zijian Liu, Wei Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Integrating Text and Image Pre-training for Multi-modal Algorithmic Reasoning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2406.05318 |