This paper proposes a Pseudo-3D Residual Network (P3D ResNet) for learning spatio-temporal video representations. The key idea is to simulate 3D convolutions using 2D spatial convolutions and 1D temporal connections, which reduces computational cost and memory usage while maintaining the ability to capture spatio-temporal information. The P3D ResNet is built by combining different variants of bottleneck blocks that leverage both spatial and temporal convolutional filters. These blocks are integrated into a residual learning framework to enhance structural diversity and improve the power of neural networks. The P3D ResNet achieves significant improvements over existing methods on the Sports-1M video classification dataset, outperforming 3D CNNs and frame-based 2D CNNs by 5.3% and 1.8%, respectively. It also demonstrates superior performance on five different benchmarks and three different tasks. The P3D ResNet is evaluated on various video understanding tasks, including action recognition, action similarity labeling, and scene recognition. It outperforms state-of-the-art methods in terms of accuracy and generalization. The P3D ResNet is shown to be effective in capturing both spatial and temporal information, with the ability to generalize well across different video analysis tasks. The architecture is validated on the Sports-1M dataset and demonstrates superior performance compared to other methods. The results indicate that the P3D ResNet is beneficial from the principle of structural diversity in network design, which enhances the power of neural networks. The paper also discusses future work, including the incorporation of attention mechanisms and the extension of P3D ResNet to other types of inputs such as optical flow or audio.This paper proposes a Pseudo-3D Residual Network (P3D ResNet) for learning spatio-temporal video representations. The key idea is to simulate 3D convolutions using 2D spatial convolutions and 1D temporal connections, which reduces computational cost and memory usage while maintaining the ability to capture spatio-temporal information. The P3D ResNet is built by combining different variants of bottleneck blocks that leverage both spatial and temporal convolutional filters. These blocks are integrated into a residual learning framework to enhance structural diversity and improve the power of neural networks. The P3D ResNet achieves significant improvements over existing methods on the Sports-1M video classification dataset, outperforming 3D CNNs and frame-based 2D CNNs by 5.3% and 1.8%, respectively. It also demonstrates superior performance on five different benchmarks and three different tasks. The P3D ResNet is evaluated on various video understanding tasks, including action recognition, action similarity labeling, and scene recognition. It outperforms state-of-the-art methods in terms of accuracy and generalization. The P3D ResNet is shown to be effective in capturing both spatial and temporal information, with the ability to generalize well across different video analysis tasks. The architecture is validated on the Sports-1M dataset and demonstrates superior performance compared to other methods. The results indicate that the P3D ResNet is beneficial from the principle of structural diversity in network design, which enhances the power of neural networks. The paper also discusses future work, including the incorporation of attention mechanisms and the extension of P3D ResNet to other types of inputs such as optical flow or audio.