RMAE-ProGRess：推进非结构化环境中的语义分割
RMAE-ProGRess: Advancing Semantic Segmentation in Unstructured Environments
提出ProGRess解码器，通过渐进式跳跃融合（PLF）实现非连续特征图的自顶向下多尺度融合，并引入轻量级通道注意力门控（LCAR）和瓶颈特征融合（BFF），为语义分割解码器设计提供了可复用的模块化思路。
原文依据 The ProGRess decoder introduces Progressive Leapwise Fusion (PLF) for top-down multi-scale fusion of non-contiguous feature maps
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论非结构化环境中的语义分割由于地形不规则、遮挡和复杂空间布局而面临独特挑战。尽管结构化场景（如城市场景）已被广泛研究，但非结构化场景的分割在标准化基准和架构设计方面仍相对缺乏探索。在本工作中，我们提出了一种基于编码器-解码器的语义分割架构，该架构集成了作为编码器的精简掩码自编码器（RMAE）、特征到金字塔（F2P）颈部以及一种名为ProGRess的新型解码器。ProGRess解码器引入了渐进式跳跃融合（PLF）用于非连续特征图的自顶向下多尺度融合，一个带残差的轻量级通道注意力门控（LCAR）模块，以及一个用于紧凑细化的瓶颈特征融合（BFF）块。我们通过在具有挑战性的非结构化环境数据集（即RELLIS-3D、其粗粒度变体和RUGD）上对最优的基于CNN和Transformer的模型进行基准测试，建立了全面的基线。我们的架构在RELLIS-3D上达到57.41%的平均交并比，在RUGD上达到45.63%的平均交并比，在RELLIS-3DC数据集上达到78.95%的平均交并比，同时保持有竞争力的参数数量和显存使用量。
英文原文摘要 1260 字符
Semantic segmentation in unstructured environments presents unique challenges due to irregular terrain, occlusions, and complex spatial layouts. While structured settings (e.g., urban scenes) have been widely studied, segmentation in unstructured settings remains relatively underexplored, both in terms of standardized benchmarking and architectural design. In this work, we propose a encoder-decoder based semantic segmentation architecture that integrates a Reduced Masked Autoencoder (RMAE) as the encoder, a Feature-to-Pyramid (F2P) neck, and a novel decoder called ProGRess. The ProGRess decoder introduces Progressive Leapwise Fusion (PLF) for top-down multi-scale fusion of non-contiguous feature maps, a Lightweight Channel Attention gate with Residuals (LCAR) module, and a Bottleneck Feature Fusion (BFF) block for compact refinement. We establish comprehensive baselines by benchmarking state-of-the-art CNN and transformer-based models on challenging unstructured environment datasets viz. RELLIS-3D, it's coarse-grained variant, and RUGD. Our architecture achieves the state-of-the-art performance with 57.41% mIoU on RELLIS-3D, 45.63% mIoU on RUGD, 78.95% mIoU on RELLIS-3DC datasets while maintaining competitive parameter-count and vRAM usage.