通过稀疏化多视角图像，提升植物表型分析的准确性与效率
ViewSparsifier: Killing Redundancy in Multi-View Plant Phenotyping
- 采用随机选择24个视角构建选择向量，减少冗余信息
- 在多视角植物表型挑战赛中双项任务夺冠
- 可扩展至120视角矩阵，适用于高精度表型建模
植物表型分析旨在通过观察植物特征理解其生长、健康与发育状态。深度学习方法常采用单视角分类或回归模型，但难以捕捉完整信息，影响植物健康评估与收获时机预测。为此，ACM Multimedia 2025的生长建模（GroMo）挑战赛提供了多视角数据集，包含多个植物及两个任务：植物年龄预测与叶片计数。每株植物从五个高度层级、多个角度拍摄，导致信息高度重叠。为学习视图不变嵌入，本文采用24个视角作为选择向量进行随机采样，提出ViewSparsifier方法，并在两项任务中均取得最佳成绩。为进一步探索，还实验了跨五级高度共120个视角的随机选择矩阵，为未来研究提供方向。
原文摘要 · Abstract (English)
Plant phenotyping involves analyzing observable characteristics of plants to better understand their growth, health, and development. In the context of deep learning, this analysis is often approached through single-view classification or regression models. However, these methods often fail to capture all information required for accurate estimation of target phenotypic traits, which can adversely affect plant health assessment and harvest readiness prediction. To address this, the Growth Modelling (GroMo) Grand Challenge at ACM Multimedia 2025 provides a multi-view dataset featuring multiple plants and two tasks: Plant Age Prediction and Leaf Count Estimation. Each plant is photographed from multiple heights and angles, leading to significant overlap and redundancy in the captured information. To learn view-invariant embeddings, we incorporate 24 views, referred to as the selection vector, in a random selection. Our ViewSparsifier approach won both tasks. For further improvement and as a direction for future research, we also experimented with randomized view selection across all five height levels (120 views total), referred to as selection matrices.
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