## 4 Experiments
### Results for the Cifar100 Datasets  
We first integrated the proposed CCGD into ResNet [13] and its variant, ResNetXt [20], to form CCGDNet, and then explored the performance gain for the Cifar100 dataset. After that, we conducted extra experiments to compare CCGDNet against other state-of-the-art attention-based methods. As with our prior instantiations, we only replaced the original \(3\times 3\) convolution with CCGD. The results in Figure 4 show that our CCGD+ResNet outperforms ResNet by a significant margin. For instance, CCGD+ResNet18 outperforms ResNet50 by a margin of 1.63%, and CCGD+ResNetXt further obtains the considerable performance improvements. The same trend can be observed for deeper networks. Table 1 shows the top-1 accuracy for the various attention model when applied to the ResNet. When compared with other state-of-the-art attention-based methods, we can see that CCGDNet achieved more advanced performance improvements. For instance, SENet achieved an accuracy of only 80.18%,whereas the baseline with CCGD achieved an accuracy of 81.36%, an increase of 1.18%. It is interesting to note that the advantage of this model not only improves network performance but also reduces the number of parameters, e.g., SENet50 (26.24 M) vs. CCGDNet50 (22.30 M), an obvious decrease of 15%.  
Figure 3: CCGD integrated with different residual block in ResNet: (**a**) basicblock; (**b**) bottleneck block.  
Table 2 shows the top-1 accuracy for the various models when applied to the Cifar100 dataset. When compared to the original backbone networks, our method improved the performance of all baseline models. In the case of ResNetXt, the performance increased from 80.78% to 81.83%, a 1.05% gain that confirmed the superiority and generalization ability of our method for general backbone networks. When compared with other state-of-the-art attention-based methods, we can see that CCGDNet achieved the best performance in several scenarios, but not in all. SE, for instance, outperformed CCGD in the case of DenseNet. This is contrary to what we would have expected. As already mentioned, a heterogeneously grouped structure is used for CCGD composition. This boosts its multi-scale representation ability. However, DenseNet and CCGD both use heterogeneously grouped structures and feature reuse mechanisms, both of which could be reformulated as a homogeneous strategy. This may, therefore, lead to over-fitting on small-scale datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Backbone** & **Params** & **Flops** & **Top-1 (\%)** \\ \hline ResNet & & 11.22 M & 0.56 G & 77.56 \\ (Baseline) & & 11.31 M & 0.57 G & 78.11 \\ SE [3] & & 11.25 M & 0.57 G & 77.77 \\ BAM [4] & ResNet18 & 11.31 M & 0.57 G & 77.94 \\ SKNet [9] & & 11.55 M & 0.57 G & 78.76 \\ GC [8] & & 11.32 M & 0.57 G & 77.92 \\ SRM [6] & & 11.23 M & 0.57 G & 78.05 \\ SC [11] & & 9.83 M & 0.60 G & 78.50 \\ CCGD(Ours) & & **9.04 M** & 0.66 G & **79.19** (\(\uparrow\) 1.63) \\ \hline ResNet & & 23.76 M & 1.31 G & 79.80 \\ (Baseline) & & 26.24 M & 1.31 G & 80.18 \\ SE [3] & & 26.28 M & 1.31 G & 80.24 \\ BAM [4] & & 26.28 M & 1.31 G & 80.45 \\ CBAM [5] & ResNet50 & 26.28 M & 1.31 G & 80.45 \\ SK [9] & & 24.03 M & 1.33 G & 80.84 \\ GC [8] & & 26.28 M & 1.32 G & 79.97 \\ SRM [6] & & 23.76 M & 1.31 G & 80.34 \\ SC [11] & & 23.71 M & **1.25 G** & 80.47 \\ CCGD(Ours) & & **22.30M** & 1.36 G & **81.36** (\(\uparrow\) 1.56) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of using different attention-based methods on Cifar100 dataset with ResNet backbone. Results in bold are the best in each column with the same backbone. \(\uparrow\) means the increase from baseline.  
Figure 4: The results of using CCGD on Cifar100 dataset with ResNet and ResNetXt backbone.