RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
\begin{table}
\centering
\begin{adjustbox}{max width=\textwidth}
\begin{tabular}{@{}c|cccc|cccc|cccc|cccc@{}}
\toprule
Methods    & \multicolumn{4}{c|}{Monolithic Proxy~tseng2019hyperparameter} & \multicolumn{4}{c|}{Cascaded Proxy~tseng2022neural} & \multicolumn{4}{c|}{CMAES~hansen2006cma,mosleh2020hardware }     & \multicolumn{4}{c}{Ours} \\ \midrule
Resolution & 720P    & 1K       & 2K       & 4K    & 720P     & 1K     & 2K     & 4K     & 720P  & 1K    & 2K    & 4K     & 720P   & 1K  & 2K  & 4K  \\
Time(s)    & 7.67    & 17.89    & 33.07    & OOM   & 70.45    & OOM    & OOM    & OOM    & 36.16 & 51.82 & 91.86 & 144.02 &   \textbf{0.27}    &   \textbf{0.33} &  \textbf{0.47}  &   \textbf{1.23}  \\ \bottomrule
\end{tabular}
\end{adjustbox}
\caption{Table 2: Experimental results demonstrating efficiency across varying input resolutions. Our method significantly outperforms other methods, achieving a speed enhancement of 260 times relative to the cascaded proxy method~tseng2022neural at 720P resolution, and 117 times faster than the CMAES approach~hansen2006cma,mosleh2020hardware at 4K resolution.}
\end{table}