用弱监督学习分析动画中的混合视觉构图，无需人工标注分割图。
Analysis of Hybrid Compositions in Animation Film with Weakly Supervised Learning
- 结合半监督与弱监督学习，无需标注即可分割混合画面。
- 在13个档案馆的短片上表现接近全监督基准。
- 适合关注动画构图分析的研究者和创作者。
我们提出一种分析动画中混合视觉构图的方法，聚焦于瞬时性电影领域。通过融合半监督与弱监督学习思想，训练出无需预标注分割掩码即可进行图像分割的模型。在来自13个电影档案馆的瞬时影片数据集上进行了评估，结果表明该学习策略性能接近全监督基线。定性分析揭示了动画中混合构图的有趣特征，为理解动画视觉语言提供了新视角。
原文摘要 · Abstract (English)
We present an approach for the analysis of hybrid visual compositions in animation in the domain of ephemeral film. We combine ideas from semi-supervised and weakly supervised learning to train a model that can segment hybrid compositions without requiring pre-labeled segmentation masks. We evaluate our approach on a set of ephemeral films from 13 film archives. Results demonstrate that the proposed learning strategy yields a performance close to a fully supervised baseline. On a qualitative level the performed analysis provides interesting insights on hybrid compositions in animation film.
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