# Grouping Bilinear Pooling for Fine-Grained Image Classification
## 4 Experiment
### Comparison with the State-of-the-Art  
Generally, it is hard to balance accuracy and the complexity of the model when bilinear pooling is applied. With the extreme compression, GBP is able to use more powerful backbones to improve performance. We embedded GBP in different backbones and compared it with other methods. The performances of baselines, full bilinear pooling based methods, compact bilinear pooling based methods, GBP (ours) methods and other state-of-the-art methods relating to channels are shown in Table 5.  
Compared with VGG-16, ResNet-50, ResNet-101 and ResNet-152, Inter-GBP improved the accuracy of the CUB dataset by 9, 3.4, 3.5 and 3.6 percentage points respectively after being applied to these backbone networks, and also improved the accuracy of the Stanford Cars dataset by 7.4, 0.7, 1.2 and 1.6 percentage points, respectively. Comparing with the methods based on bilinear pooling and compact bilinear pooling, GBP is the most compact method and achieves the best performance. Especially on the Stanford Cars dataset, Inter-GBP improves the best performance of compact bilinear pooling from 91.80% to 94.22%. In addition, due to lack of simplicity and convenience, few papers apply compact bilinear pooling approaches to high-performing backbones. We also tried to perform other compact BP on better backbones, but the result is not good enough. GBP compresses the bilinear representation to the extreme so that it can be used with more powerful backbones to achieve competitive performance. The grouping operation of GBP is performed at the channel level. Compared with other state-of-the-art methods relating to channels [16; 36; 37; 38; 39], GBP shows a performance as good as or even better than these methods. The spatially weighted pooling (SWP) [36] strategy was proposed to improve the robustness and effectiveness of the feature representation, compared with SWP, GBP can improve the performance of the model better when combined with different backbones. Ref. [37] devised a novel model Hierarchical Bilinear Pooling with Aggregated Slack Mask (HBPASM) to generate a RoI-aware image feature representation for better performance, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & **Dimension** & **Parameters** & **CUB (\%)** & **Stanford Cars (\%)** \\ \hline VGG-16 [27] & - & 25 K & 20 MB & 74.59 & 85.05 \\ ResNet-50 [28] & - & 2 K & 1.6 MB & 82.15 & 92.19 \\ ResNet-101 [28] & - & 2 K & 1.6 MB & 82.58 & 92.56 \\ ResNet-152 [28] & - & 2 K & 1.6 MB & 82.74 & 92.64 \\ \hline FBP [15] & & & & 84.01 & 91.18 \\ iFBP [32] & VGG-16 & 260 K & 200 MB & 85.80 & **92.10** \\ MoNet-FBP [33] & & & & **86.40** & 91.80 \\ \hline CBP [17] & & 10 K & 8 MB & 84.00 & 90.19 \\ LBRP [19] & & 10 K & 0.8 MB & 84.21 & **90.90** \\ MoNet-TS [33] & VGG-16 & 10 K & 8 MB & **85.70** & 90.80 \\ FBC [34] & & 8 K & 6.4 MB & 84.30 & - \\ SBP-EN [35] & & 10 K & 8 MB & 84.50 & **90.90** \\ \hline  & VGG-16 & - & - & - & 90.70 \\ SWP [36] & ResNet-50 & - & - & - & 92.30 \\  & ResNet-101 & - & - & - & 93.10 \\ HBPASM [37] & ResNet-34 & - & - & 86.80 & 92.80 \\ HBP [16] & VGG-16 & 24 K & 19 MB & 87.01 & 93.70 \\ SFF [38] & VGG-16 & - & - & 81.10 & 88.30 \\ ResNet-50 & - & - & **87.30** & **94.00** \\ MC-loss [39] & ResNet-50 & - & - & **87.30** & 93.70 \\ \hline  & VGG-16 & 1 K & 0.8 MB & 83.66 & 92.49 \\
**Inter-GBP** & ResNet-50 & 2 K & 1.6 MB & 85.54 & 92.86 \\  & ResNet-101 & 2 K & 1.6 MB & 86.10 & 93.76 \\  & ResNet-152 & 2K & 1.6 MB & **86.31** & **94.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The performances of different methods on CUB dataset and Stanford Cars dataset. From top to bottom, the five blocks respectively list the baselines, full bilinear pooling based methods, compact bilinear pooling based methods, other state-of-the-art methods relating to channels and our method. ref.