用于轻量级语义分割的多级特征连续聚合解码方案
A Decoding Scheme with Successive Aggregation of Multi-Level Features for Light-Weight Semantic Segmentation
AI总结：
本文提出一种基于多级视觉 Transformer 的轻量语义分割解码方案，通过连续交叉注意力聚合多级特征并用聚合语义进行增强，在不损失精度的前提下显著降低计算成本。
摘要：
包括分层视觉 Transformer 在内的多尺度架构，已被普遍应用于高分辨率语义分割，以在最小性能损失的情况下应对计算复杂性。本文据此提出一种新颖的语义分割解码方案，它从采用多尺度架构的编码器中获取多级特征。该基于多级视觉 Transformer 的解码方案通过在多级特征聚合中引入连续交叉注意力，不仅旨在降低计算开销，还旨在获得更高的分割精度。此外，本文还提出了一种利用聚合语义增强多级特征的方法。相关工作聚焦于从注意力分配的角度保持上下文一致性，从而以显著更低的计算成本带来性能提升。在常用数据集上进行的一系列实验表明，与最先进的语义分割模型相比，所提方案在计算成本方面具有优势，且不会损失精度；大量消融研究也证明了所提思路的有效性。
英文摘要：
Multi-scale architecture, including hierarchical vision transformer, has been commonly applied to high-resolution semantic segmentation to deal with computational complexity with minimum performance loss. In this paper, we propose a novel decoding scheme for semantic segmentation in this regard, which takes multi-level features from the encoder with multi-scale architecture. The decoding scheme based on a multi-level vision transformer aims to achieve not only reduced computational expense but also higher segmentation accuracy, by introducing successive cross-attention in aggregation of the multi-level features. Furthermore, a way to enhance the multi-level features by the aggregated semantics is proposed. The effort is focused on maintaining the contextual consistency from the perspective of attention allocation and brings improved performance with significantly lower computational cost. Set of experiments on popular datasets demonstrates superiority of the proposed scheme to the state-of-the-art semantic segmentation models in terms of computational cost without loss of accuracy, and extensive ablation studies prove the effectiveness of ideas proposed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
