We employ a Graph Attention Network (GAT) variant: [ h_i^(l+1) = \sigma\left( \sum_j \in \mathcalN(i) \alpha_ij W^(l) h_j^(l) \right) ] where ( \alpha_ij ) are attention coefficients computed via shared learnable weights. After ( L=3 ) layers, a global pooling operation (max + mean) produces an image-level embedding ( z_img \in \mathbbR^512 ).
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