# A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance
## 4 Experiment
### Comparison to State-of-the-Art and Analysis  
In this section, our model is compared with the state-of-the-art semantic segmentation methods, including SERT [5], SegNext [7], FLANet [37], etc., on three datasets  
Figure 7: The decoder network architecture of MCAG. (ADE20K [29], Cityscapes [30], and COCO-Stuff [31]) to demonstrate the superiority of the proposed approach. The multi-scale flipping testing strategy (MS) is employed during the comparison process. On ADE20K, we compare MCAG with state-of-the-art semantic segmentation models. As shown in Table 1, MCAG achieves a nearly 1.0% higher mIoU compared to the state-of-the-art CNN-based model SegNext-B [7], and it outperforms the fully attentional network FLANet [37] by 0.7% in mIoU. Additionally, MCAG achieves better mIoU values than the Transformer-based models MPViT [22], FASeg [38] and TSG [39] with fewer parameters. FASeg introduces a simple and effective query design for semantic segmentation called dynamic focus-aware position query (DFPQ), which dynamically generates position queries based on the cross-attention scores of the previous decoder block and the position encoding of corresponding image features. TSG, on the other hand, utilizes internal attributes of the attention map in Transformer for multi-scale feature selection in semantic segmentation. TSG introduces TSGE and TSGD in the encoder and decoder of the Transformer, respectively, to enhance the semantic segment localization performance. These results demonstrate that MCAG achieves competitive segmentation performance while introducing a multi-path self-attention mechanism at a lower computational cost than Transformer models. The asterisk (*) denotes reproduced results. On Cityscapes, we compare MCAG with state-of-the-art semantic segmentation models. As shown in Table 2, MCAG achieves a 0.51% higher mIoU compared to Segnext-B [7] on Cityscapes, surpasses FLANet [37] by 2.81% in mIoU and outperforms the Transformer-based models FASeg [38] and TSG [39] by 4.01% and 6.71% in mIoU, respectively. Moreover, MCAG achieves a 1.91% higher mIoU than PIDNet-L [44] with a lower parameter count. Additionally, StructToken [42] introduces a human-centric perspective to semantic segmentation, proposing the StructToken with structural prior (StructToken-PWE) model, which generates a coarse mask for each class based on structural priors and then progressively refines the mask. MCAG outperforms StructToken-PWE by 0.44% in mIoU. These results demonstrate that the multiple pathways in MCAG used for global information fusion and the handling of boundary information and details are highly effective. With the guidance from the main pathway, MCAG maintains excellent segmentation performance at a significantly lower computational cost than Transformer-based models. The asterisk (*) denotes reproduced results. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Params (M)** & **Backbone** & **mIoU (\%)** \\ \hline Segformer-B0 [6] & 3.8 & MiT & 38.0 \\ MaskFormer [40]* & 42 & Swin & 46.5 \\ Segformer-B1 [6] & 13.7 & MiT & 43.1 \\ HRFormer-S [14] & 13.5 & - & 45.1 \\ HRFormer-B [14]* & 56.2 & - & 45.9 \\ AFFormer-base [41] & 3.0 & - & 41.8 \\ SegNext-B [7]* & 27.6 & MSCAN-B & 46.72 \\ SETR-MLA-DeiT [5] & 92.59 & T-Base & 46.15 \\ StructToken-PWE [42]* & 38 & ViT-S/16 & 46.6 \\ FLANet [37] & - & HRNetW48 & 46.99 \\ MPViT [22]* & 52 & MPViT-S & 46.52 \\ FASeg [38]* & 51 & R50 & 47.5 \\ TSG [39] & 72 & Swin-T & 47.5 \\ MPCNet [43] & - & R101 & 38.04 \\ MCAG (OURS) & 36 & MSCAN-B & 47.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with SOTA on ADE20K. The asterisk (*) denotes reproduced results. On COCO-Stuff, as shown in Table 3, MCAG achieves a 0.9% improvement in mIoU compared with SERT. The asterisk (*) denotes reproduced results. Additionally, to highlight the good performance of MCAG concerning details and boundaries, we compare the segmentation results (mIoU %) of MCAG and SegNext on some small objects in ADE20K, including Mirror, Seat, Lamp, Box, Book, Pillow and Oven.