C2F-CHART: A Curriculum Learning Approach to Chart Classification

2024-09-07

International Conference on Pattern Recognition

AI Summary

To address the challenge of fine-grained classification in scientific chart accessibility, this paper proposes a coarse-to-fine curriculum learning framework grounded in inter-class similarity, emulating human cognitive progression through dynamically constructed, incremental training sequences. Methodologically, it integrates hierarchical category modeling, feature-space similarity measurement, and fine-tuning of deep classification networks to enable controllable, stage-wise training. Its key innovation lies in explicitly modeling inter-class similarity as the principled basis for curriculum design—establishing, to the best of our knowledge, the first generalizable curriculum learning paradigm for chart classification. Evaluated on the ICPR 2022 CHART-Infographics dataset, the method substantially outperforms prior state-of-the-art approaches, achieving significant gains in fine-grained classification accuracy. This work advances scientific chart understanding and contributes a novel, principled approach to improving accessibility for visually impaired users.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Reasoning under Uncertainty: Graphical Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• Improving chart accessibility through classification optimization
• Applying curriculum learning for chart classification tasks
• Enhancing performance on chart datasets beyond current benchmarks

Innovation

• Uses coarse-to-fine curriculum learning
• Exploits inter-class similarities
• Outperforms state-of-the-art results