  Domain generalized semantic segmentation (DGSS) is a critical yet challenging
task, where the model is trained only on source data without access to any
target data. Despite the proposal of numerous DGSS strategies, the
generalization capability remains limited in CNN architectures. Though some
Transformer-based segmentation models show promising performance, they
primarily focus on capturing intra-sample attentive relationships, disregarding
inter-sample correlations which can potentially benefit DGSS. To this end, we
enhance the attention modules in Transformer networks for improving DGSS by
incorporating information from other independent samples in the same batch,
enriching contextual information, and diversifying the training data for each
attention block. Specifically, we propose two alternative intra-batch attention
mechanisms, namely mean-based intra-batch attention (MIBA) and element-wise
intra-batch attention (EIBA), to capture correlations between different
samples, enhancing feature representation and generalization capabilities.
Building upon intra-batch attention, we introduce IBAFormer, which integrates
self-attention modules with the proposed intra-batch attention for DGSS.
Extensive experiments demonstrate that IBAFormer achieves SOTA performance in
DGSS, and ablation studies further confirm the effectiveness of each introduced
component.
