Self-supervised monocular depth estimation with hybrid CNN-transformer architecture using skip attention mechanism
A hybrid self-supervised model incorporating a Skip Attention Mechanism to minimize semantic inconsistencies between encoder and decoder, and a mixed pooling method combining max and average pooling to address over-smoothing and contextual information loss is proposed.