用170类手绘草图数据集，对比多种分类方法，模型表现超人类。
Comparative Analysis of Different Methods for Classifying Polychromatic Sketches
- 构建170类手绘草图数据集，评测多种机器学习分类方法。
- 最佳模型Top-1准确率达47.5%，超越人类41%的水平。
- 适合研究草图识别、低级视觉理解与人机性能对比的读者。
图像分类是计算机视觉中的重要挑战，尤其在人类不熟悉的领域。随着机器学习和人工智能的发展，算法需具备与人类相当甚至超越人类的视觉能力。为此，我们收集、清洗并解析了一个大规模的手绘涂鸦数据集，并对比了多种机器学习方法在170个类别上的分类性能。最终最佳模型达到47.5%的Top-1准确率，显著优于人类在该数据集上的41%表现。
原文摘要 · Abstract (English)
Image classification is a significant challenge in computer vision, particularly in domains humans are not accustomed to. As machine learning and artificial intelligence become more prominent, it is crucial these algorithms develop a sense of sight that is on par with or exceeds human ability. For this reason, we have collected, cleaned, and parsed a large dataset of hand-drawn doodles and compared multiple machine learning solutions to classify these images into 170 distinct categories. The best model we found achieved a Top-1 accuracy of 47.5%, significantly surpassing human performance on the dataset, which stands at 41%.
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