共享结构提升解码器性能，让深度估计更准
Rethinking Encoder-Decoder Flow Through Shared Structures
- 用共享结构替代传统逐块解码，增强上下文信息
- 在自然与合成图像上均提升深度估计精度
- 适合研究视觉任务中解码器设计的学者
密集预测任务的编码器架构日益复杂，而解码器仍多采用逐块顺序处理中间特征图的方式。本文提出使用共享结构（banks），由每个解码块共同利用，通过重采样和特征融合提供额外上下文信息。该方法在基于Transformer的先进架构上，于大规模数据集训练下，显著提升了自然图像与合成图像上的深度估计性能。
原文摘要 · Abstract (English)
Dense prediction tasks have enjoyed a growing complexity of encoder architectures, decoders, however, have remained largely the same. They rely on individual blocks decoding intermediate feature maps sequentially. We introduce banks, shared structures that are used by each decoding block to provide additional context in the decoding process. These structures, through applying them via resampling and feature fusion, improve performance on depth estimation for state-of-the-art transformer-based architectures on natural and synthetic images whilst training on large-scale datasets.
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