# DA-GAN: Dual Attention Generative Adversarial Network for Cross-Modal Retrieval
## 5 Experiments
### Experimental Results
#### 5.4.3 Results on Pascal Sentences Dataset  
The Comparison of mAP scores of DA-GAN and the 13 state-of-the-arts on Pascal Sentences dataset are shown in Table 4. Once again, DA-GAN is the winner in this contest, which achieves I2T mAP = 72.9%, T2I mAP = 73.5% and average mAP = 73.2%, defeats the runner-up DSCMR (I2T mAP = 71.0%, T2I mAP = 72.2%, average mAP = 71.6%) by 2.5%, 1.3% and 1.6%, respectively. Different from the above comparisons, CM-GANs (I2T mAP = 61.2%, T2I mAP = 61.0%, average mAP = 61.1%) performs worse than DA-GAN and DSCMR evidently. As analyzed above, the performance improvement mainly comes from the integration of intra- and inter-modal attention as well as adversarial learning.  
Figures 8-10 illustrate the I2T, T2I and average mAP scores of each approaches on 20 categories on Pascal Sentences dataset, respectively. For both I2T and T2I tasks, all these approaches have poor cross-modal retrieval performance in some categories, such as \"bottle\" and \"chair\". It is mainly because the objects in these categories are relatively small. By contrast, the precisions on \"aeroplane\", \"bird\", \"cat\", \"horse\", \"motorbike\", \"sheep\" and \"train\" are obviously higher since these samples contain much more discriminative semantic features. Specifically, for the I2T task, the mAP of DA-GAN reaches nearly 90%, 91% and 92% on \"aeroplane\", \"cat\" and \"train\", respectively. For the T2I task, it achieves nearly 92%, 93%, and 95% on these three categories. From Figure 10 we observe that the semantic recognition performance of DA-GAN is the best among these 14 approaches.  
Figure 6(c),f show the PR curves of DA-GAN and 13 state-of-the-arts for I2T and T2I tasks, respectively. On both tasks, it is clear that the changing of performance of DA-GAN and CM-GANs are very similar. Although CM-GANs show good performance, they cannot  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Traditional Method** & **I2T** & **T2I** & **Aver.** \\ \hline CCA [69] & 22.5 & 22.7 & 22.6 \\ KCCA [11] & 43.3 & 39.8 & 41.6 \\ MCCA [70] & 66.4 & 48.9 & 55.45 \\ MvDA [71] & 59.4 & 62.6 & 61.0 \\ MvDA-VC [72] & 64.8 & 67.3 & 66.1 \\ JRL [73] & 52.7 & 53.4 & 53.1 \\ \hline
**Deep Learning-Based Method** & **I2T** & **T2I** & **Aver.** \\ \hline DCCA [42] & 67.8 & 67.7 & 67.8 \\ DCCAE [24] & 68.0 & 67.1 & 67.5 \\ CCL [74] & 57.6 & 56.1 & 56.9 \\ CMDN [75] & 54.4 & 52.6 & 53.5 \\ ACMR [57] & 67.1 & 67.6 & 67.3 \\ DSCMR [44] & 71.0 & 72.2 & 71.6 \\ CM-GANs [76] & 61.2 & 61.0 & 61.1 \\ \hline
**The Proposed Method** & **I2T** & **T2I** & **Aver.** \\ \hline DA-GAN & **72.9** & **73.5** & **73.2** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison results (mAP@50 in %) with 13 competitors on Pascal Sentences dataset. The best performance values are in bold-font.  
overcome our method. For the I2T task, the precision of DA-GAN declines slowly when the recall increases from 0.2 to 0.8. After that, it drops sharply. In contrast, the performance of our method shows a significant downward trend for the T2I task, but it is still the best.