C2F-CHART: A Curriculum Learning Approach to Chart Classification
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916782541897728 |
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| author | Shaheen, Nour Elsharnouby, Tamer Torki, Marwan |
| author_facet | Shaheen, Nour Elsharnouby, Tamer Torki, Marwan |
| contents | In scientific research, charts are usually the primary method for visually representing data. However, the accessibility of charts remains a significant concern. In an effort to improve chart understanding pipelines, we focus on optimizing the chart classification component. We leverage curriculum learning, which is inspired by the human learning process. In this paper, we introduce a novel training approach for chart classification that utilizes coarse-to-fine curriculum learning. Our approach, which we name C2F-CHART (for coarse-to-fine) exploits inter-class similarities to create learning tasks of varying difficulty levels. We benchmark our method on the ICPR 2022 CHART-Infographics UB UNITEC PMC dataset, outperforming the state-of-the-art results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04683 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | C2F-CHART: A Curriculum Learning Approach to Chart Classification Shaheen, Nour Elsharnouby, Tamer Torki, Marwan Computer Vision and Pattern Recognition In scientific research, charts are usually the primary method for visually representing data. However, the accessibility of charts remains a significant concern. In an effort to improve chart understanding pipelines, we focus on optimizing the chart classification component. We leverage curriculum learning, which is inspired by the human learning process. In this paper, we introduce a novel training approach for chart classification that utilizes coarse-to-fine curriculum learning. Our approach, which we name C2F-CHART (for coarse-to-fine) exploits inter-class similarities to create learning tasks of varying difficulty levels. We benchmark our method on the ICPR 2022 CHART-Infographics UB UNITEC PMC dataset, outperforming the state-of-the-art results. |
| title | C2F-CHART: A Curriculum Learning Approach to Chart Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.04683 |