# Human Action Representation Learning Using an Attention-Driven Residual 3 DCNN Network
## 4 Results and Discussion
### Comparison with the State-of-the-Art Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Year** & **Accuracy (\%)** \\ \hline Multi-task hierarchical clustering [33] & 2017 & 89.7 \\ BT-LSTM [34] & 2018 & 85.3 \\ Deep autoencoder [35] & 2019 & 96.2 \\ Two-stream attention LSTM [36] & 2020 & 96.9 \\ Weighted entropy-variances based feature selection [37] & 2021 & 94.5 \\ Dilated CNN+BiLSTM+RB [38] & 2021 & 89.0 \\ DS-GRU [39] & 2021 & 97.1 \\ Local-global features + QSVM [32] & 2021 & 82.6 \\ Squeezed CNN [40] & 2022 & 87.4 \\ Fusion-based discriminative features [31] & 2022 & 97.8 \\ BS-2 SCN [41] & 2022 & 90.1 \\
3 DCNN [42] & 2022 & 85.1 \\ DA-R3 DCNN (Proposed) & 2023 & **98.6** \\ \hline \hline \end{tabular} Bold value represents the best accuracy, where the italic value indicates the runner up accuracy.  
\end{table} 
Table 3: Comparative analysis of the proposed DA-R3 DCNN with the state-of-the-art methods on the UCF11 dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Year** & **Accuracy (\%)** \\ \hline Multi-task hierarchical clustering [33] & 2017 & 93.2 \\ Deep autoencoder [35] & 2019 & 96.4 \\ Ensemble model with swarm-based optimization [43] & 2021 & 92.2 \\ DS-GRU [39] & 2021 & 95.2 \\ Local-global features + QSVM [32] & 2021 & 69.4 \\ Hybrid Deep Evolving Neural Networks [44] & 2022 & 77.3 \\ ViT + Multi Layer LSTM [45] & 2022 & 96.1 \\
3 DCNN [42] & 2022 & 82.6 \\ DA-R3 DCNN (Proposed) & 2023 & **97.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of the proposed DA-R3 DCNN with the state-of-the-art methods on the HMDB51 dataset. In the case of the challenging HMDB51 dataset, the proposed DA-R3 DCNN achieved the highest accuracy of 82.5%, surpassing all other comparative methods considered in our assessments. The Evidential deep learning method [56] emerged as the runner-up with an accuracy of 77.0%. Among the comparative methods, Multi-task hierarchical clustering [33] achieved the lowest accuracy of 51.4% on the HMDB51 dataset. Other comparative methods included STPP + LSTM [46], TSN [48], Deep autoencoder [35], TS-LSTM + temporal-inception [50], HATNet [51], Correlational CNN + LSTM [52], STDAN [53], DB-LSTM + SSPF [54], DS-GRU [39], TCLC [55], Semi-supervised temporal gradient learning [57], BS-2 SCN [41], ViT + Multi Layer LSTM [45], and MAT-EffNet [58]. These methods achieved accuracies of 70.5%, 72.2%, 70.7%, 58.6%, 70.3%, 69.0%, 74.8%, 66.2%, 56.5%, 75.1%, 72.3%, 71.5%, 75.9%, 71.3%, 73.7%, and 70.9%, respectively. From the list of comparative assessments in Table 5, the proposed DA-R3 DCNN achieved an average improvement of 19.01%, in terms of accuracy over the average results of the state-of-the-art methods on the HMDB51 dataset. Finally, for the UCF101 dataset, the results listed in Table 6 demonstrate that the proposed DA-R3 DCNN surpassed all other comparative methods by achieving the highest accuracy of 97.8%. The Ensemble model with swarm-based optimization method [43] secured the runner-up position with an accuracy of 96.3%. On the UCF101 dataset, the Multi-task hierarchical clustering [33] obtained the lowest accuracy of 76.3% among all the comparative methods. Additional comparative methods included Saliency-aware 3 DCNN with LSTM [59], Spatio-temporal multilayer networks [60], Long-term temporal convolutions [20], CNN + Bi-LSTM [8], OFF [61], TVNet [62], Attention cluster [63], Videolstm [17], Two stream convnets [64], Mixed 3D-2D convolutional tube [65], TS-LSTM + Temporal-inception [50], TSN + TSM [66], STM [67], Correlational CNN + LSTM [52], ResCNN-DBLSTM [68], SC-BDLSTM [69], BS-2 SCN [41], TDS-BiLSTM [70], META-RGB + Flow [71], and Spurious-3D Residual Network [72]. These methods achieved accuracies of 84.0%, 87.0%, 82.4%, 92.8%, 96.0%, 95.4%, 94.6%, 89.9%, 88.9%, 91.1%, 94.3%, 96.2%, 92.8%, 94.7%, 94.2%, 90.1%, 94.7%, 96.0%, and 95.6%, respectively. Further