## 4 Experiments
### Results for the ImageNet Datasets  
Next, we explored whether the superior performance of our method could be generalized to other datasets apart from Cifar100. Comparative experiments were therefore carried out using the ImageNet dataset. Due to space limitations, we only report here the detailed metrics for ResNet [13]. Table 3 shows that, when compared to other attention methods, CCGDNet achieved a better performance while using fewer parameters, i.e., for SENet50, BAMNet50, and SRMNet50, the top-1 accuracies were 0.76%, 0.97%, and 0.74% better, respectively. Clear advantages were also visible in the loss curves (see Figure 5). We also explored the effect of using different network depths on the ImageNet dataset. As can be seen in Table 3, as the depth increased, the performance of CCGDNet steadily surpassed the other competitors, given the same configuration. These results demonstrate the effectiveness of CCGD for complex datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Backbone**} \\ \cline{2-4}  & **ResNetXt** & **DenseNet** & **DLANet** \\ \hline Baseline & 80.78 & 80.24 & 80.43 \\ SE & 81.40 & **80.66** & - \\ BAM & 81.15 & 80.48 & - \\ SK & - & 80.24 & 80.79 \\ SGE & 81.35 & 80.08 & 80.74 \\ CCGD(Ours) & **81.83** & 80.53 & **81.10** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparisons of different models on the Ciafr100 dataset. Results in bold are the best in each column.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Backbone** & **Years** & **Parameters** & **FLOPS** & **Top-1 (\%)** & **Top-5 (\%)** \\ \hline ResNet (baseline) & & CVPR-16 & 11.69 M & 1.82 G & 69.83 & 89.10 \\ SE [3] & & CVPR-18 & 11.78 M & 1.82 G & 70.86 & 89.78 \\ BAM [4] & & BMVC-18 & 11.71 M & 1.83 G & 71.12 & 89.99 \\ CBM [5] & & ECCV-18 & 11.78 M & **1.82 G** & 70.73 & 89.91 \\ TA [27] & & WACV-21 & 11.69 M & 1.83 G & 71.19 & 89.99 \\ CCGD (Ours) & & & **9.16** M & 2.05 G & **71.22** (\(\uparrow\) 1.39) & **90.03** (\(\uparrow\) 0.93) \\ \hline ResNet (baseline) & & CVPR-16 & 25.56 M & 4.12 G & 76.40 & 92.94 \\ SENet [3] & & CVPR-18 & 28.08 M & 4.13 G & 77.11 & 93.40 \\ BAM [4] & & BMVC-18 & 25.92 M & 4.21 G & 76.90 & 93.66 \\ CBM [5] & & ECCV-18 & 28.09 M & 4.13 G & 77.34 & 93.69 \\ GALA [28] & & ICLR-19 & 29.40 M & - & 77.27 & 93.66 \\ GC [8] & & CVPR-19 & 28.10 M & 4.13 G & 77.70 & 93.76 \\ SK [9] & & CVPR-19 & 26.15 M & 4.19 G & 77.54 & 93.62 \\ SKM [6] & & CVPR-19 & 25.62 M & 4.12 G & 77.13 & 93.70 \\ SC [11] & & CVPR-20 & 25.60 M & **3.95 G** & 77.52 & 93.78 \\ TA [27] & & WACV-21 & 25.56 M & 4.17 G & 77.48 & 93.68 \\ CCGD (Ours) & & **24.15** M & 4.27 G & **77.87**(\(\uparrow\) 1.47) & **93.95**(\(\uparrow\) 1.01) \\ \hline ResNet (baseline) & & CVPR-16 & 44.46 M & 7.84 G & 78.20 & 93.91 \\ SE [3] & & CVPR-18 & 49.32 M & 7.86 G & 78.46 & 94.10 \\ BAM [4] & & BMVC-18 & 44.91 M & 7.93 G & 78.46 & 94.02 \\ CBAM [5] & & ECCV-18 & 49.33 M & 7.86 G & 78.49 & 94.31 \\ SK [9] & & CVPR-19 & 45.68 M & 7.98 G & 78.79 & 94.26 \\ SRM [6] & & CVPR-19 & 44.68 M & 7.85 G & 78.47 & 93.75 \\ SC [11] & & CVPR-20 & 44.56 M & **7.20 G** & 78.60 & 93.98 \\ TA [27] & & WACV-21 & 44.56 M & 7.95 G & 78.03 & 93.77 \\ CCGD (Ours) & & & **41.89** M & - & **78.84**(\(\uparrow\) 0.64) & **94.32**(\(\uparrow\) 0.41) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of using different attention-based methods on ImageNet dataset with ResNet backbone. Results in bold are the best in each column with the same backbone. \(\uparrow\) means the increase from baseline.