On scarce dual-view pasture data, a simple two-layer gated depthwise convolution fusion achieves R²=0.903, beating cross-view attention transformers (0.833), bidirectional SSMs (0.819), and Mamba (0.793), while backbone pretraining scale dominates all other choices.
Combining computer vision and interactive spatial statistics for the characterization of precision agriculture observations,
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Fusion Complexity Inversion: Why Simpler Cross View Modules Outperform SSMs and Cross View Attention Transformers for Pasture Biomass Regression
On scarce dual-view pasture data, a simple two-layer gated depthwise convolution fusion achieves R²=0.903, beating cross-view attention transformers (0.833), bidirectional SSMs (0.819), and Mamba (0.793), while backbone pretraining scale dominates all other choices.