# Proxy-Based Semi-Supervised Cross-Modal Hashing
## 4 Experiments
### Performance Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**MIRFLICKR-25K**} & \multicolumn{3}{c}{**NUS-WIDE**} & \multicolumn{3}{c}{**MS COCO**} \\ \cline{3-11}  & & **16 Bits** & **32 Bits** & **64 Bits** & **16 Bits** & **32 Bits** & **64 Bits** & **16 Bits** & **32 Bits** & **64 Bits** \\ \hline \multirow{1}{*}{I\(\rightarrow\)T} & DGCPN (AAAI 21) & 0.703 & 0.713 & 0.720 & 0.566 & 0.589 & 0.601 & 0.575 & 0.613 & 0.630 \\  & UCCH (TPAMI 23) & 0.734 & 0.741 & 0.739 & 0.590 & 0.610 & 0.618 & 0.562 & 0.569 & 0.590 \\  & LEMON (MM 20) & 0.651 & 0.670 & 0.682 & 0.460 & 0.491 & 0.507 & 0.492 & 0.438 & 0.527 \\  & EDMH (TKDE 22) & 0.651 & 0.657 & 0.646 & 0.460 & 0.477 & 0.461 & 0.502 & 0.497 & 0.427 \\  & HMAH (TMM 22) & 0.755 & 0.743 & 0.753 & 0.606 & 0.636 & 0.639 & 0.558 & 0.569 & 0.594 \\  & HCCCH (TMM 23) & 0.719 & 0.730 & 0.736 & 0.625 & 0.638 & 0.649 & 0.560 & 0.606 & 0.634 \\  & MGCH (IS 22) & 0.689 & 0.705 & 0.729 & 0.525 & 0.514 & 0.595 & 0.615 & 0.562 & 0.607 \\  & SSCH (TCSVT 23) & 0.622 & 0.670 & 0.685 & 0.479 & 0.524 & 0.539 & 0.435 & 0.441 & 0.479 \\  & TS3H (TNNLS 23) & 0.717 & 0.741 & 0.742 & 0.613 & 0.642 & 0.671 & 0.618 & 0.624 & 0.690 \\  & GCSCH (MM 24) & 0.772 & 0.776 & 0.785 & 0.658 & 0.677 & 0.673 & 0.619 & 0.675 & 0.701 \\  & PSSCH (Ours) & **0.794** & **0.797** & **0.818** & **0.666** & **0.678** & **0.684** & **0.644** & **0.689** & **0.723** \\ \hline \multirow{1}{*}{T\(\rightarrow\)I} & DGCPN (AAAI 21) & 0.692 & 0.701 & 0.710 & 0.578 & 0.596 & 0.601 & 0.572 & 0.609 & 0.625 \\  & UCCH (TPAMI 23) & 0.722 & 0.726 & 0.725 & 0.600 & 0.616 & 0.626 & 0.553 & 0.560 & 0.586 \\  & LEMON (MM 20) & 0.666 & 0.695 & 0.708 & 0.472 & 0.508 & 0.517 & 0.487 & 0.475 & 0.535 \\  & EDMH (TKDE 22) & 0.668 & 0.677 & 0.667 & 0.475 & 0.487 & 0.477 & 0.501 & 0.494 & 0.427 \\  & HMAH (TMM 22) & 0.721 & 0.703 & 0.705 & 0.546 & 0.578 & 0.559 & 0.549 & 0.558 & 0.578 \\  & HCCCH (TMM 23) & 0.721 & 0.740 & 0.742 & 0.631 & 0.632 & 0.649 & 0.556 & 0.588 & 0.647 \\  & MGCH (IS 22) & 0.675 & 0.695 & 0.719 & 0.541 & 0.515 & 0.607 & 0.601 & 0.553 & 0.586 \\  & SSCH (TCSVT 23) & 0.623 & 0.664 & 0.688 & 0.482 & 0.526 & 0.557 & 0.440 & 0.443 & 0.474 \\  & TS3H (TNNLS 23) & 0.727 & 0.753 & 0.748 & 0.622 & 0.653 & 0.674 & 0.614 & 0.618 & 0.687 \\  & GSCSCH (MM 24) & **0.780** & **0.791** & 0.791 & 0.661 & 0.673 & 0.684 & 0.620 & 0.661 & 0.688 \\  & PSSCH (Ours) & 0.774 & 0.787 & **0.803** & **0.671** & **0.683** & **0.692** & **0.657** & **0.702** & **0.728** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of mAP with baseline methods on MIRFLICKR-25K, NUS-WIDE, and MS COCO based on 30% labeled samples. The best results are in bold font. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task** & **Dataset** & \(t\)**-Statistic** & \(p\)**-Value** & **Conclusion** \\ \hline \multirow{3}{*}{I\(\rightarrow\)T} & MIRFLICKR-25K & 19.42 & \(p\) \(<\) 0.001 & \multirow{3}{*}{PSSCH is significantly better than GCSCH} \\  & NUS-WIDE & 5.74 & \(p\) \(<\) 0.001 & \multirow{3}{*}{PSSCH is significantly better than GCSCH} \\  & MS COCO & 15.25 & \(p\) \(<\) 0.001 & \multirow{3}{*}{PSSCH is significantly better than GCSCH} \\ \cline{1-1} \cline{3-3}  & MIRFLICKR-25K & 11.00 & \(p\) \(<\) 0.001 & \multirow{3}{*}{PSSCH is significantly better than GCSCH} \\ \cline{1-1}  & NUS-WIDE & 9.08 & \(p\) \(<\) 0.001 & \multirow{3}{*}{PSSCH is significantly better than GCSCH} \\ \cline{1-1}  & MS COCO & 29.03 & \(p\) \(<\) 0.001 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Paired \(t\)-test results comparing mAP values of PSSCH with GCSCH on MIRFLICKR-25K, NUS-WIDE, and MS COCO at 64 bits.