# DA-GAN: Dual Attention Generative Adversarial Network for Cross-Modal Retrieval
## 5 Experiments
### Experimental Results  
#### 5.4.1 Results on Wikipedia Dataset  
The mAP scores of DA-GAN and the 13 competitors on the Wikipedia dataset are reported in Table 2. For both I2T and T2I tasks, the proposed DA-GAN outperforms all these state-of-the-arts by 54.3% and 63.9% respectively, higher than the two best competitors, i.e., DSCMR [44] (I2T mAP = 52.1%) and CM-GANs [76] (T2I mAP = 62.1%). Besides, the average mAP of DA-GAN is the highest, which is 3% higher than CM-GANs. The main reason is that the combination of intra- and inter-modal attention captures more single-modal and cross-modal semantic correlations. Although both DSCMR and CM-GANs extract the semantic information by supervised learning, they do not learn the inter-modal semantic correlation effectively to realize cross-modal semantic augmentation. On the other hand, except for DCCA and DCCAE whose mAPs (I2T mAP = 44.4% and 43.5%, T2I mAP = 39.6% and 38.5%) are a bit lower than JRL (I2T mAP = 44.9%, T2I mAP = 41.8%).  
Figures 4 and 5 illustrate the I2T and T2I mAP scores of each category on Wikipedia dataset. The average mAP scores are shown in Figure 6. Obviously, for all these approaches, there are big differences between the retrieval precisions of different categories. Specifically, for both I2T and T2I tasks, the performances on \"biology\", \"geography & places\", \"sport & recreation\" and \"warfare\" are better than other categories. That is mainly because the samples in the above categories are semantically independent of other categories, and have more obvious distinguishing features than other categories. In contrast, the categories \"art & architecture\", \"history\" and \"royalty & nobility\" are relative to each other in abstract semantics. The samples of the categories have more confusing features.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Traditional Method** & **I2T** & **T2I** & **Aver.** \\ \hline CCA [69] & 13.4 & 13.3 & 13.4 \\ KCCA [11] & 19.8 & 18.6 & 19.2 \\ MCCA [70] & 34.1 & 30.7 & 32.4 \\ MMDA [71] & 33.7 & 30.8 & 32.3 \\ MvDA-VC [72] & 38.8 & 35.8 & 37.3 \\ JRL [73] & 44.9 & 41.8 & 43.4 \\ \hline
**Deep Learning-Based Method** & **I2T** & **T2I** & **Aver.** \\ \hline DCCA [42] & 44.4 & 39.6 & 42.0 \\ DCCAE [24] & 43.5 & 38.5 & 41.0 \\ CCL [74] & 50.4 & 45.7 & 48.1 \\ CMDN [57] & 48.7 & 42.7 & 45.7 \\ ACMR [57] & 47.7 & 43.4 & 45.6 \\ DSCMR [44] & 52.1 & 47.8 & 49.9 \\ CM-GANs [76] & 50.0 & 62.1 & 56.1 \\ \hline
**The Proposed Method** & **I2T** & **T2I** & **Aver.** \\ \hline DA-GAN & **54.3** & **63.9** & **59.1** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison results (mAP@50 in %) with 13 competitors on Wikipedia dataset. The best performance values are in bold-font.  
From Figures 4 and 5, it is clear that DA-GAN has better semantic recognition ability. For example, the highest IZT and T2I mAP scores of DA-GAN on \"biology\", \"sport & recreation\" and \"warfare\" are near 83% and 85%, higher than the competitive rivals such as DSCMR (I2T mAP = 78%, T2I mAP = 73%), CCL (I2T mAP = 73%, T2I mAP = 69%) and CM-GANs (I2T mAP = 74%, T2I mAP = 82%).  
Figure 7a,d show the IZT and T2I precisions of DA-GAN and the competitors on different recalls, respectively. For both I2T and T2I tasks, DA-GAN has the highest precision at all levels of recall, which exhibits the performance improvement by adversarial learning with a dual attention mechanism. DSCMR and CM-GANs are still the most two competitive rivals, but they cannot defeat DA-GAN at any recall value.  
Figure 4: The mAP of I2T task of each category on Wikipedia dataset for our method DA-GAN and the competitors.  
Figure 5: The mAP of T2I task of each category on Wikipedia dataset for our method DA-GAN and the competitors.  
Figure 6: The average mAP of each category on Wikipedia dataset for our method DA-GAN and the competitors.