融合复杂性倒置：为何更简单的跨视图模块在牧草生物量回归中优于SSM和跨视图注意力变换器
Fusion Complexity Inversion: Why Simpler Cross View Modules Outperform SSMs and Cross View Attention Transformers for Pasture Biomass Regression
AI总结：
本文研究了在稀少农业数据下，简单跨视图模块在牧草生物量回归中的优越性，发现模型复杂度与性能呈反比关系，提出应优先考虑模型基础架构质量而非融合复杂度。
摘要：
准确估计农业图像中的牧草生物量对于可持续牲畜管理至关重要，但现有方法受限于实际监控中数据量小、不平衡和标注稀疏的问题。本研究系统评估了将视觉基础模型适应于农业回归的方法，使用CSIRO牧草生物量基准测试集（357张图像双视图数据集，具有实验室验证的五个生物量目标的组件级真实值），通过17种配置（四类主干网络EfficientNet-B3到DINOv3-ViT-L、五种跨视图融合机制和4x2元数据因子）进行评估。发现一个反直觉原则——
英文摘要：
Accurate estimation of pasture biomass from agricultural imagery is critical for sustainable livestock management, yet existing methods are limited by the small, imbalanced, and sparsely annotated datasets typical of real world monitoring. In this study, adaptation of vision foundation models to agricultural regression is systematically evaluated on the CSIRO Pasture Biomass benchmark, a 357 image dual view dataset with laboratory validated, component wise ground truth for five biomass targets, through 17 configurations spanning four backbones (EfficientNet-B3 to DINOv3-ViT-L), five cross view fusion mechanisms, and a 4x2 metadata factorial. A counterintuitive principle, termed "fusion complexity inversion", is uncovered: on scarce agricultural data, a two layer gated depthwise convolution (R^2 = 0.903) outperforms cross view attention transformers (0.833), bidirectional SSMs (0.819), and full Mamba (0.793, below the no fusion baseline). Backbone pretraining scale is found to monotonically dominate all architectural choices, with the DINOv2 -> DINOv3 upgrade alone yielding +5.0 R^2 points. Training only metadata (species, state, and NDVI) is shown to create a universal ceiling at R^2 ~ 0.829, collapsing an 8.4 point fusion spread to 0.1 points. Actionable guidelines for sparse agricultural benchmarks are established: backbone quality should be prioritized over fusion complexity, local modules preferred over global alternatives, and features unavailable at inference excluded.
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