# DBCAN: DFormer-Based Cross-Attention Network for RGB Depth Semantic Segmentation
## 4 Results
### RGB-D Semantic Segmentation Model for Comparison
#### 4.4.1 NYUDepthv2 Semantic Segmentation  
As presented in Table 3, DBCAN is compared with several previous methods [7; 10; 16; 28; 29; 30; 31; 32; 33; 34; 35] on the NYUDepthv2 validation set. We selected various ResNet-based and efficient Convolutional Neural Network (CNN)-based methods as baselines. \begin{table}
\begin{tabular}{c c c c c} \hline
**Config** & **DBCAN-T** & **DBCAN-S** & **DBCAN-B** & **DBCAN-L** \\ \hline input size & \(480\times 480/480\times 480\) & \(480\times 480/480\times 480\) & \(480\times 480/480\times 480\) & \(480\times 480/480\times 480\) \\ optimizer & AdamW & AdamW & AdamW & AdamW \\ batch size & \(8/16\) & \(8/16\) & \(8/16\) & \(8/16\) \\ base learning rate & \(6\times 10^{-5}/8\times 10^{-5}\) & \(6\times 10^{-5}/8\times 10^{-5}\) & \(6\times 10^{-5}/8\times 10^{-5}\) & \(6\times 10^{-5}/8\times 10^{-5}\) \\ weight decay & \(0.01\) & \(0.01\) & \(0.01\) & \(0.01\) \\ epochs & \(500/300\) & \(500/300\) & \(500/300\) & \(500/300\) \\ optimizer momentum & \(\beta 1,\beta 2=0.9,0.999\) & \(\beta 1,\beta 2=0.9,0.999\) & \(\beta 1,\beta 2=0.9,0.999\) & \(\beta 1,\beta 2=0.9,0.999\) \\ learning rate schedule & linear decay & linear decay & linear decay & linear decay \\ warmup epochs & \(10\) & \(10\) & \(10\) & \(10\) \\ stochastic depth & \(0.1/0.1\) & \(0.1/0.1\) & \(0.1/0.1\) & \(0.15/0.15\) \\ \hline \end{tabular}
\end{table} 
Table 2: DBCAN fine-tuning settings on NYUDepthv2/SUNRGD. The input size, batch size, base learning rate, epochs, and stochastic depth are specified for NYUDepthv2/SUN-RGBD datasets. All other hyperparameters, such as optimizer, weight decay, optimizer momentum, learning rate schedule, and warmup epochs, are shared across both datasets. Among these ResNet-based baselines, PGDENet [33] achieves good results with Acc (78.1), mAcc (66.7), and mIoU (53.7). DBCAN-L achieves higher Acc (80.3), mAcc (70.5), and mIoU (57.4) compared to PGDENet. The superiority of DBCAN over ResNet-based models can be attributed to the multiple attention operations added in each stage of DFormer, which enable more effective feature extraction. Additionally, the utilization of multiple efficient attention operations in the decoder of DBCAN contributes to more efficient decoding and better prediction results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Backbone** & **Params** & **Input Size** & **Flops** & **Acc** & **mAcc** & **mIoU** \\ \hline ACNet [7] & ResNet-50 & 116.6 M & 530 \(\times\) 730 & 163.9 G & - & - & 48.1 \\ SCNet [16] & ResNet-101 & 64.7 M & 530 \(\times\) 730 & 151.5 G & - & - & 48.6 \\ SA-Gate [28] & ResNet-101 & 110.9 M & 530 \(\times\) 730 & 250.1 G & - & - & 49.4 \\ CEN [29] & ResNet-101 & 118.2 M & 530 \(\times\) 730 & 618.7 G & 82.8 & 61.9 & 50.2 \\ CEN [29] & ResNet-152 & 133.9 M & 530 \(\times\) 730 & 664.4 G & 83.3 & 63.0 & 50.9 \\ ShapeConv [30] & ResNet-101 & 86.8 M & 530 \(\times\) 730 & 124.6 G & 82.2 & 59.2 & 48.6 \\ ESANet [31] & ResNet-34 & 31.2 M & 480 \(\times\) 640 & 34.9 G & - & - & 48.2 \\ FRNet [32] & ResNet-34 & 46.9 M & 530 \(\times\) 730 & 45.4 G & - & - & 48.4 \\ CMX [34] & MiT-B2 & 66.6 M & 530 \(\times\) 730 & 86.3 G & - & - & 49.7 \\ CMX [34] & MiT-B4 & 139.9 M & 530 \(\times\) 730 & 173.8 G & - & - & 52.1 \\ CMX [34] & MiT-B5 & 181.1 M & 530 \(\times\) 730 & 217.6 G & - & - & 52.4 \\ TopFormer [10] & DFormer-T & 6.0 M & 530 \(\times\) 730 & 15.1 G & 82.5 & 60.1 & 48.7 \\ TopFormer [10] & DFormer-S & 18.7 M & 530 \(\times\) 730 & 33.0 G & 82.9 & 61.6 & 50.0 \\ Topformer [10] & DFormer-B & 29.5 M & 530 \(\times\) 730 & 54.0 G & 83.1 & 62.9 & 51.0 \\ Topformer [10] & DFormer-L & 39.0 M & 530 \(\times\) 730 & 83.3 G & 83.6 & 63.9 & 52.2 \\ \hline DBCAN & DFormer-T & 22.7 M & 530 \(\times\) 730 & 87.3 G & 82.6 & 60.3 & 48.9 \\ DBCAN & DFormer-S & 38.1 M & 530 \(\times\) 730 & 113.7 G & 83.0 & 62.0 & 50.3 \\ DBCAN & DFormer-B & 48.8 M & 530 \(\times\) 730 & 129.9 G & 83.4 & 63.0 & 51.2 \\ DBCAN & DFormer-L & 59.2 M & 530 \(\times\) 730 & 158.1 G & 83.6 & 64.9 & 52.6 \\ \hline \hline \end{tabular}
\end{table}