# Establishing Two-Dimensional Dependencies for Multi-Label Image Classification
## 4 Experiments
### Experimental Results
#### 4.4.1 Comparisons with State-of-the-Art Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Methods** & **Aero** & **Bike** & **Bird** & **Boat** & **Bot** & **Bots** & **Car** & **Cat** & **Chhair** & **Cow** & **Table** & **Dog** & **Horse** & **Mot** & **Pers** & **Plant** & **Sheep** & **Sofa** & **Train** & **Tv** & **mAP** \\ \hline CNN-RNN [39] & 96.7 & 83.1 & 94.2 & 92.8 & 61.2 & 82.1 & 89.1 & 94.2 & 64.2 & 83.6 & 70.0 & 92.4 & 91.7 & 84.2 & 93.7 & 59.8 & 93.2 & 75.3 & 99.7 & 78.6 & 84.0 \\ ResNet-101 [26] & 99.5 & 97.7 & 97.8 & 96.4 & 65.7 & 91.8 & 96.1 & 96.7 & 74.2 & 80.9 & 85.0 & 98.4 & 96.5 & 95.9 & 98.4 & 70.1 & 88.3 & 80.2 & 98.9 & 89.9 & 89.2 \\ FeV+LV [50] & 97.9 & 97.0 & 96.6 & 94.6 & 73.6 & 93.9 & 95.6 & 95.5 & 73.7 & 90.3 & 82.8 & 95.4 & 97.7 & 95.9 & 98.6 & 77.6 & 88.7 & 78.0 & 89.8 & 89.0 & 90.6 \\ Atten-Reinforte [51] & 98.6 & 97.1 & 97.1 & 95.5 & 75.6 & 92.8 & 96.8 & 97.3 & 78.3 & 93.2 & 87.6 & 96.9 & 96.5 & 93.6 & 98.5 & 81.6 & 93.1 & 83.2 & 98.5 & 89.3 & 92.0 \\ Attl. [52] & 99.0 & 97.2 & 96.6 & 96.2 & 75.4 & 92.0 & 96.8 & 97.2 & 79.0 & 93.6 & 89.3 & 97.0 & 97.5 & 94.0 & 99.8 & 81.6 & 94.3 & 85.8 & 98.7 & 90.6 & 92.5 \\ ML-CCN [12] & 99.5 & 98.5 & 98.6 & 98.1 & 80.8 & 97.2 & 98.2 & 82.3 & 95.7 & 86.4 & 98.2 & 98.4 & 96.7 & 99.0 & 84.7 & 96.7 & 84.3 & 98.9 & 93.7 & 94.0 \\ CNN-SGSG [43] & 99.6 & 98.3 & 98.0 & 97.5 & 81.0 & 93.1 & 97.5 & 86.5 & 86.3 & 88.3 & 89.2 & 95.5 & 98.0 & 96.1 & 98.3 & 89.0 & 96.7 & 91.6 & 97.9 & 92.3 & 94.2 \\ LGG [19] & 99.6 & 95.5 & 97.3 & 96.4 & 80.5 & 98.1 & 98.9 & 86.9 & 86.8 & 98.7 & 98.6 & 96.9 & 99.8 & 84.8 & 97.2 & 98.8 & 98.7 & 93.8 & 98.6 & 94.2 \\ FLNet [53] & 99.6 & 98.7 & 98.9 & 97.8 & 94.6 & 95.3 & 96.2 & 96.5 & 88.6 & 96.1 & 87.2 & 97.7 & 98.6 & 97.0 & 98.1 & 86.5 & 97.4 & 86.5 & 98.8 & 90.8 & 94.4 \\ GTMC [18] & 99.7 & 98.4 & 98.0 & 98.3 & 96.5 & 97.5 & 98.8 & 83.1 & 96.1 & 87.4 & 98.6 & 98.9 & 97.2 & 99.0 & 85.4 & 97.1 & 84.9 & 99.2 & 94.2 & 94.7 \\ SGGRL [14] & 99.7 & 98.4 & 98.0 & 97.6 & 85.7 & 96.2 & 98.8 & 82.0 & 98.1 & 98.7 & 98.7 & 98.7 & 97.0 & 99.0 & 86.9 & 98.1 & 85.8 & 93.0 & 93.7 & 95.0 \\ VSGN [48] & **99.8** & 98.6 & 98.7 & 98.6 & 85.2 & 99.9 & 82.6 & 86.6 & 83.6 & 96.5 & 87.8 & 99.1 & **99.0** & 97.5 & 99.3 & 85.8 & 96.3 & 87.6 & 98.6 & 95.2 & 95.1 \\ MnCon [47] & **99.8** & **98.3** & **99.3** & 98.6 & 83.3 & **98.4** & 98.0 & 98.3 & 85.8 & **98.3** & 90.5 & **99.3** & 98.9 & 96.6 & 98.8 & 86.3 & **99.8** & 87.3 & 98.9 & 96.1 & 95.6 \\ Ours & 99.6 & **99.1** & 98.8 & **98.9** & **85.8** & 97.9 & **98.1** & **97.9** & 89.1 & **92.9** & 99.1 & 98.6 & **96.6** & **99.3** & **90.9** & 99.8 & **91.3** & **99.8** & **97.0** & **96.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of the AP and mAP with the state-of-the-art methods on the VOC 2007 dataset. (Bold indicates the best performance in its category, and the same applies hereafter). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Methods** & **Aero** & **Bike** & **Bird** & **Boat** & **Bot** & **Bus** & **Car** & **Cat** & **Chhair** & **Cow** & **Table** & **Dog** & **Horse** & **Mot** & **Pers** & **Plant** & **Sheep** & **Sofa** & **Train** & **Tv** & **mAP** \\ \hline Fev+LV [50] & 98.4 & 92.8 & 93.4 & 90.7 & 74.9 & 93.2 & 90.2 & 96.1 & 78.2 & 89.8 & 80.6 & 95.7 & 96.1 & 95.3 & 97.5 & 73.1 & 91.2 & 75.4 & 97.0 & 88.2 & 89.4 \\ HCP [54] & 99.1 & 92.8 & 97.4 & 94.4 & 79.9 & 93.6 & 89.8 & 98.2 & 78.2 & 94.9 & 79.8 & 97.8 & 97.0 & 93.8 & 96.4 & 74.3 & 94.7 & 71.9  
**Comparisons on MS-COCO. ** The COCO dataset is a large and diverse dataset for object detection, segmentation, and captioning. It aims at scene understanding and is primarily composed of images captured from complex everyday scenes, with precise object localization achieved through accurate segmentation. The dataset consists of 91 object categories, 328,000 images, and 2,500,000 labels.