SCIENTIA SINICA Informationis, Volume 51 , Issue 5 : 779(2021) https://doi.org/10.1360/SSI-2019-0120

## A cross-media search method for social networks based on adversarial learning and semantic similarity

• AcceptedSep 4, 2019
• PublishedApr 13, 2021
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### References

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• Figure 1

(Color online) The general flowchart of the proposed SSACR method

• Figure 2

(Color online) PR curve of image to text (a), text to image (b) search/Wikipedia dataset

• Figure 3

(Color online) PR curve of image to text (a), text to image (b) search/Weibo dataset

• Figure 4

(Color online) The performance of SSACR with different model parameter $k$ on Wikipedia dataset (a) and Weibo dataset (b)

• Figure 5

(a) The performance of image to text search, (b) textto image search, and (c) the average performance with different model parameters/Wikipedia dataset

• Figure 6

(a) The performance of image to text search, (b) textto image search, and (c) the average performance with different model parameters/Weibo dataset

• Figure 7

The embed and domain loss during the training process

• Figure 8

(Color online) Example of cross-media retrieval on Weibo dataset

• Table 1   Statistics of the datasets in our experiments
 Training instance Test instance Labels Image feature Text feature Wikipedia dataset 2173 693 10 4096d VGG19 5000d BoW Weibo dataset 4262 473 5 4096d VGG19 6739d BoW
•

Algorithm 1 Training process of SSACR

Require:Based on mini-batch, extract the feature of the current batch data, image feature as $\{~{v_1},\ldots,{v_n}\}~$, text feature as $\{~{t_1},\ldots,{t_n}\}~$, semantic distribution as $\{~{l_1},\ldots,{l_n}\}~$; Output: Trained ${\theta~_V}$ and ${\theta~_T}$;

Output:The number of iterations for feature projection network's single training is $k$, data amount is $m$ for each mini-batch, learning rate is $\mu~$, loss parameter is $\lambda~$;

Randomly initialize the parameters of the model;

while not converge do

while $k~>~0$ do

Optimize ${\theta~_V}$, ${\theta~_T}$ and ${\theta~_{\rm~imd}}$ in the direction of decreasing gradient;${\theta~_V}~=~{\theta~_V}~-~\mu~~\cdot~{\nabla~_{{\theta~_V}}}\frac{1}{m}({L_{\rm~emb}}~-~{L_{\rm~adv}})$;${\theta~_T}~=~{\theta~_T}~-~\mu~~\cdot~{\nabla~_{{\theta~_T}}}\frac{1}{m}({L_{\rm~emb}}~-~{L_{\rm~adv}})$;${\theta~_{\rm~imd}}~=~{\theta~_{\rm~imd}}~-~\mu~~\cdot~{\nabla~_{{\theta~_{\rm~imd}}}}\frac{1}{m}({L_{\rm~emb}}~-~{L_{\rm~adv}})$;

$k~\Leftarrow~k~-~1$;

end while

Optimize ${\theta~_D}$ in the direction of increasing gradient;${\theta~_D}~=~{\theta~_D}~+~\mu~~\cdot~\lambda~~\cdot~{\nabla~_{{\theta~_D}}}\frac{1}{m}({L_{\rm~emb}}~-~{L_{\rm~adv}})$;

end while

Return ${\theta~_V}$ and ${\theta~_T}$.

•

Algorithm 2 Cross-modal search process

Require:Search item $x$, image data $V$, text data $T$; Output: Search result list res;

Output:The projection vector of search item is $s$, the projection matrix of data which has different models with search item is $R$, similarity matrix between $s$ and $R$ is $S$;

if $x$ is text then

$s~=~{f_T}(x;{\theta~_T})$;

$R~=~{f_V}(V;{\theta~_V})$;

else

$s~=~{f_V}(x;{\theta~_V})$;

$R~=~{f_T}(T;{\theta~_T})$;

end if

$S~=~{\rm~sim}(s,R)$;

${\rm~res}~=~{\rm~argsort}(S)[:\text{top-}K]$;

Return res.

• Table 2   Comparison of cross-media retrieval performance on Wikipedia dataset
 map@5 map@20 map@50 txt2img img2txt Average txt2img img2txt Average txt2img img2txt Average CCA 0.2685 0.2151 0.2418 0.2831 0.2209 0.2520 0.2543 0.2178 0.2361 JFSSL 0.4406 0.3473 0.3940 0.4264 0.3576 0.3920 0.4146 0.3454 0.3800 CMDN 0.5094 0.4125 0.4609 0.4895 0.4102 0.4498 0.4624 0.3956 0.4290 ACMR 0.6225 0.4987 0.5606 0.6109 0.4986 0.5548 0.5732 0.4835 0.5284 DSCMR 0.6342 0.4982 0.5662 0.6421 0.5012 0.5716 0.6347 0.4901 0.5624 Ours 0.6604 0.4964 0.5784 0.6647 0.5052 0.5850 0.6436 0.4964 0.5700
• Table 3   Comparison of cross-media retrieval performance on Weibo dataset
 map@5 map@20 map@50 txt2img img2txt Average txt2img img2txt Average txt2img img2txt Average CCA 0.3885 0.3251 0.3568 0.3583 0.3239 0.3411 0.3213 0.3199 0.3206 JFSSL 0.6478 0.5351 0.5915 0.6128 0.5181 0.5655 0.5197 0.5282 0.5239 CMDN 0.7183 0.5814 0.6499 0.6799 0.5843 0.6321 0.5906 0.5729 0.5817 ACMR 0.8653 0.7133 0.7893 0.8238 0.7071 0.7655 0.7065 0.6992 0.7029 DSCMR 0.8742 0.7177 0.7960 0.8192 0.7246 0.7719 0.7410 0.7267 0.7339 Ours 0.8792 0.7344 0.8068 0.8161 0.7361 0.7761 0.7493 0.7410 0.7452
• Table 4   Comparison of SSACR performance on Wikipedia dataset composed with different loss
 map@5 map@20 map@50 txt2img img2txt Average txt2img img2txt Average txt2img img2txt Average ${L_{\rm~imd}}$ only 0.6591 0.4859 0.5725 0.6677 0.4946 0.5811 0.6468 0.4916 0.5692 ${L_{\rm~imi}}$ only 0.5118 0.5031 0.5075 0.5287 0.5055 0.5171 0.5100 0.4974 0.5037 Both 0.6604 0.4964 0.5784 0.6647 0.5052 0.5850 0.6436 0.4964 0.5700
• Table 5   Comparison of SSACR performance on Weibo dataset composed with different loss
 map@5 map@20 map@50 txt2img img2txt Average txt2img img2txt Average txt2img img2txt Average ${L_{\rm~imd}}$ only 0.6082 0.6842 0.6462 0.7094 0.6476 0.6785 0.7819 0.6577 0.7198 ${L_{\rm~imi}}$ only 0.6853 0.5031 0.5942 0.6891 0.5055 0.5973 0.6905 0.4974 0.5940 Both 0.8792 0.7344 0.8068 0.8161 0.7361 0.7761 0.7493 0.7410 0.7452

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