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.
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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.