Tighnari v2: 通过专家混合和弱监督学习缓解多模态植物分布预测中的标签噪声和分布偏移
Tighnari v2: Mitigating Label Noise and Distribution Shift in Multimodal Plant Distribution Prediction via Mixture of Experts and Weakly Supervised Learning
AI总结：
Tighnari v2通过专家混合和弱监督学习缓解多模态植物分布预测中的标签噪声和分布偏移，提升预测性能。
摘要：
大规模、跨物种的植物分布预测在生物多样性保护中起着至关重要的作用，然而在这一领域中的建模努力仍然面临显著挑战，这是因为观测数据的稀疏性和偏倚性。存在-缺失（PA）数据提供准确且无噪声的标签，但获取成本高且数量有限；存在-only（PO）数据则提供广泛的空间覆盖和丰富的时空分布，但负样本中存在严重的标签噪声。为了解决这些现实约束，本文提出了一种多模态融合框架，充分利用了PA和PO数据的优势。我们引入了一种基于卫星影像地理覆盖的创新伪标签聚合策略，使标签空间与遥感特征空间实现地理对齐。在模型架构方面，我们采用Swin Transformer Base作为卫星影像的主干网络，利用TabM网络进行表格特征提取，保留Temporal Swin Transformer用于时间序列建模，并采用可堆叠的串行三模态交叉注意力机制来优化异构模态的融合。此外，实证分析揭示了PA训练和测试样本之间存在显著的地理分布偏移，并且直接混合PO和PA数据的模型由于PO数据中的标签噪声而容易出现性能下降。为了解决这一问题，我们借鉴了专家混合范式：测试样本根据其与PA样本的空间接近程度进行分组，并在每个分组中使用在不同数据集上训练的不同模型进行推理和后处理。在GeoLifeCLEF 2025数据集上的实验表明，我们的方法在PA覆盖有限且分布偏移显著的场景中实现了优越的预测性能。
英文摘要：
Large-scale, cross-species plant distribution prediction plays a crucial role in biodiversity conservation, yet modeling efforts in this area still face significant challenges due to the sparsity and bias of observational data. Presence-Absence (PA) data provide accurate and noise-free labels, but are costly to obtain and limited in quantity; Presence-Only (PO) data, by contrast, offer broad spatial coverage and rich spatiotemporal distribution, but suffer from severe label noise in negative samples. To address these real-world constraints, this paper proposes a multimodal fusion framework that fully leverages the strengths of both PA and PO data. We introduce an innovative pseudo-label aggregation strategy for PO data based on the geographic coverage of satellite imagery, enabling geographic alignment between the label space and remote sensing feature space. In terms of model architecture, we adopt Swin Transformer Base as the backbone for satellite imagery, utilize the TabM network for tabular feature extraction, retain the Temporal Swin Transformer for time-series modeling, and employ a stackable serial tri-modal cross-attention mechanism to optimize the fusion of heterogeneous modalities. Furthermore, empirical analysis reveals significant geographic distribution shifts between PA training and test samples, and models trained by directly mixing PO and PA data tend to experience performance degradation due to label noise in PO data. To address this, we draw on the mixture-of-experts paradigm: test samples are partitioned according to their spatial proximity to PA samples, and different models trained on distinct datasets are used for inference and post-processing within each partition. Experiments on the GeoLifeCLEF 2025 dataset demonstrate that our approach achieves superior predictive performance in scenarios with limited PA coverage and pronounced distribution shifts.
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