# Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection
## 4 Experiments
### Error Analyses
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Backbone** & **Schedule** & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AP_{5}\) & \(AP_{M}\) & \(AP_{L}\) \\ \hline Faster R-CNN * & ResNet50-DCN & 12 & 41.3 & 62.4 & 45.0 & 24.6 & 44.9 & 54.4 \\ Faster R-CNN * & ResNet101-DCN & 12 & 42.7 & 63.8 & 46.4 & 24.9 & 46.7 & 56.8 \\ Faster R-CNN * & ResNeXt101-32\(\times\)4d & 12 & 41.2 & 62.1 & 45.1 & 24.0 & 45.5 & 53.5 \\ Faster R-CNN * & ResNeXt101-64\(\times\)4d & 12 & 42.1 & 63.0 & 46.3 & 24.8 & 46.2 & 55.3 \\ Mask R-CNN * & ResNet50-DCN & 12 & 41.8 & 62.7 & 46.2 & 24.5 & 45.3 & 55.4 \\ Mask R-CNN * & ResNet101-DCN & 12 & 43.5 & 64.3 & 47.9 & 25.7 & 47.7 & 57.5 \\ Mask R-CNN * & ResNetXt101-32\(\times\)4d & 12 & 41.9 & 62.5 & 45.9 & 24.4 & 46.3 & 54.0 \\ Cascade R-CNN * & ResNet50-DCN & 12 & 43.8 & 62.6 & 47.9 & 26.3 & 47.2 & 58.5 \\ Cascade R-CNN * & ResNeXt101-32\(\times\)4d & 12 & 43.7 & 62.3 & 47.7 & 25.1 & 47.6 & 57.3 \\ DETR[4] & ResNet50 & 500 & 42.0 & 62.4 & 44.2 & 20.5 & 45.8 & 61.1 \\ DETR[4] & ResNet101 & 500 & 43.5 & 63.8 & 46.4 & 21.9 & 48.0 & 61.8 \\ Deformable DETR[26] & ResNet50 & 50 & 43.8 & 62.6 & 47.7 & 26.4 & 47.1 & 58.0 \\ Sparse R-CNN[27] & ResNet101 & 36 & 44.1 & 62.1 & 47.2 & 26.1 & 46.3 & 59.7 \\ HITC * & ResNet101 & 20 & 44.8 & 63.3 & 48.8 & 25.7 & 48.5 & 60.2 \\ Mask R-CNN * & Swin-Tiny & 36 & 46.0 & 68.2 & 50.3 & 30.5 & 49.2 & 59.5 \\ HITC * & ResNeXt101-32\(\times\)4d & 20 & 46.1 & 65.3 & 50.1 & 27.1 & 49.6 & 60.9 \\ Mask R-CNN *† & Swin-Small & 36 & 48.2 & 69.8 & 52.8 & 32.1 & 51.8 & 62.7 \\ \hline MSBA Faster R-CNN & ResNet50-DCN & 12 & 42.2 & 63.3 & 46.2 & 25.3 & 46.0 & 55.7 \\ MSBA Faster R-CNN & ResNet101-DCN & 12 & 43.4 & 64.4 & 47.5 & 25.7 & 47.4 & 57.8 \\ MSBA Faster R-CNN & ResNeXt101-32\(\times\)4d & 12 & 42.1 & 63.3 & 45.7 & 24.7 & 46.6 & 54.8 \\ MSBA Faster R-CNN & ResNeXt101-64\(\times\)4d & 12 & 43.0 & 64.3 & 47.1 & 25.3 & 46.9 & 56.9 \\ MSBA Mask R-CNN & ResNet50-DCN & 12 & 43.1 & 63.9 & 47.4 & 25.8 & 47.1 & 57.0 \\ MSBA Mask R-CNN & ResNet101-DCN & 12 & 44.2 & 64.9 & 48.4 & 25.9 & 48.3 & 58.5 \\ MSBA Mask R-CNN & ResNeXt101-32\(\times\)4d & 12 & 43.1 & 64.0 & 46.9 & 26.2 & 47.1 & 56.2 \\ MSBA Cascade R-CNN & ResNet50-DCN & 12 & 44.6 & 63.6 & 48.8 & 27.0 & 48.2 & 59.3 \\ MSBA Cascade R-CNN & ResNeXt101-32\(\times\)4d & 12 & 44.2 & 63.0 & 47.8 & 25.4 & 48.4 & 58.3 \\ MSBA HITC & ResNet101 & 20 & 45.7 & 64.7 & 49.6 & 27.0 & 49.5 & 60.6 \\ MSBA Mask R-CNN * & Swin-Tiny & 36 & 47.1 & 68.8 & 51.5 & 31.9 & 50.2 & 60.6 \\ MSBA HITC & ResNeXt101-32\(\times\)4d & 20 & 46.9 & 66.4 & 51.2 & 28.6 & 50.6 & 61.7 \\ MSBA Mask R-CNN * & Swin-Small & 36 & **48.7** & **70.6** & **53.5** & **32.8** & **52.5** & **63.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparisons with the states of the art: The symbol _***_ signifies our re-implemented results on mmdetection. “Schedule” refers to the learning schedules of the respective methods. The † symbol indicates models trained with additional tricks, such as multi-scale training. Figure 6: The error analyses of four categories: The results in the first row correspond to the baseline, while those in the second row correspond to MSBA.