Video Prediction by Efficient Transformers (2212.06026v1)

Abstract: Video prediction is a challenging computer vision task that has a wide range of applications. In this work, we present a new family of Transformer-based models for video prediction. Firstly, an efficient local spatial-temporal separation attention mechanism is proposed to reduce the complexity of standard Transformers. Then, a full autoregressive model, a partial autoregressive model and a non-autoregressive model are developed based on the new efficient Transformer. The partial autoregressive model has a similar performance with the full autoregressive model but a faster inference speed. The non-autoregressive model not only achieves a faster inference speed but also mitigates the quality degradation problem of the autoregressive counterparts, but it requires additional parameters and loss function for learning. Given the same attention mechanism, we conducted a comprehensive study to compare the proposed three video prediction variants. Experiments show that the proposed video prediction models are competitive with more complex state-of-the-art convolutional-LSTM based models. The source code is available at https://github.com/XiYe20/VPTR.

• The paper presents the VidHRFormer block that decouples spatial and temporal attention to significantly reduce computational complexity.
• The paper evaluates three VPTR variants (fully, partially, and non-autoregressive), with VPTR-NAR achieving superior LPIPS scores on benchmark datasets.
• The paper demonstrates that efficient Transformer architectures can rival ConvLSTM models, opening new avenues in video representation learning and self-supervised analysis.

Video Prediction by Efficient Transformers: A Technical Overview

"Video Prediction by Efficient Transformers," authored by Xi Ye and Guillaume-Alexandre Bilodeau, continues the exploration of advanced models in video prediction, a domain within computer vision tasked with anticipating future video frames from existing sequences. The authors propose a suite of Transformer-based models that introduce a unique local spatial-temporal separation attention mechanism, aimed at reducing the computational complexity traditionally associated with Transformers, which have become popular for their success in NLP tasks.

Model Contributions

1. VidHRFormer Block: At the core of the proposed models is the VidHRFormer block, which efficiently processes spatio-temporal features by integrating spatial local attention and temporal attention in a two-step process. This novel block reduces the complexity of handling input features from $\mathcal{O}((THW)^2d_{model})$ to $\mathcal{O}((\frac{H^2W^2}{P^2} + T^2)d_{model})$, which significantly enhances computational feasibility.
2. Variants of Video Prediction Transformers (VPTR): Three distinct models are constructed based on the aforementioned VidHRFormer block:
   • VPTR-FAR (Fully Autoregressive Model): This traditional approach relies on predicting each future frame conditioned on the entire history of previous frames.
   • VPTR-PAR (Partially Autoregressive Model): A variant that combines autoregressive processing with a parallelization component by structuring dependencies within the future frames.
   • VPTR-NAR (Non-Autoregressive Model): This variant discards direct temporal dependencies, leveraging a non-autoregressive Transformer framework that enhances inference speed and diminishes error propagation.

Evaluation and Performance

The empirical evaluation of these models spans three datasets: KTH, MovingMNIST, and BAIR, providing a robust set of benchmarks. The reported results suggest that the VPTR models, especially VPTR-NAR, deliver competitive performance against state-of-the-art convolutional LSTM-based approaches. The models excel particularly in LPIPS scores, indicating superior perceptual quality in the generated video frames.

• KTH Dataset: VPTR variants display impressive LPIPS results, with VPTR-NAR outperforming its counterparts in maintaining frame quality.
• MovingMNIST: The models hold their ground in SSIM values, although challenges remain in handling complex overlapping digit scenarios.
• BAIR Dataset: Highlighting the challenges of high stochastic environments, VPTR models achieve performance comparable to existing techniques, with VPTR-NAR matching several metric benchmarks.

Technical Insights and Implications

This work contributes primarily to the field of video representation learning by demonstrating that an efficient Transformer architecture can match, if not surpass, the performance of more complex and traditionally dominant ConvLSTM frameworks. The refinements in handling spatio-temporal data introduce a versatile tool, potentially applicable to various video processing tasks beyond sequence prediction, such as anomaly detection and model-based reinforcement learning.

Future Directions

The efficient handling of video data using Transformers posits potential expansions. Future research could aim to refine the deterministic nature of VPTR by integrating stochastic modeling layers to handle high variance datasets better. Moreover, these models could facilitate advancements in self-supervised learning in video analysis, with further exploration warranted into adapting these models for different visual feature scales and domains.

In conclusion, "Video Prediction by Efficient Transformers" illustrates a notable shift towards streamlined yet potent machine learning models in computer vision, reinforcing the adaptable nature of Transformer-based approaches across interdisciplinary applications.