Learning Video Representations without Natural Videos (2410.24213v2)

Abstract: We show that useful video representations can be learned from synthetic videos and natural images, without incorporating natural videos in the training. We propose a progression of video datasets synthesized by simple generative processes, that model a growing set of natural video properties (e.g., motion, acceleration, and shape transformations). The downstream performance of video models pre-trained on these generated datasets gradually increases with the dataset progression. A VideoMAE model pre-trained on our synthetic videos closes 97.2\% of the performance gap on UCF101 action classification between training from scratch and self-supervised pre-training from natural videos, and outperforms the pre-trained model on HMDB51. Introducing crops of static images to the pre-training stage results in similar performance to UCF101 pre-training and outperforms the UCF101 pre-trained model on 11 out of 14 out-of-distribution datasets of UCF101-P. Analyzing the low-level properties of the datasets, we identify correlations between frame diversity, frame similarity to natural data, and downstream performance. Our approach provides a more controllable and transparent alternative to video data curation processes for pre-training.

• The paper shows that synthetic video datasets, enriched with natural image crops, nearly close the performance gap on UCF101 action classification.
• It proposes a sequential increase in dataset complexity, where added motion and transformation dynamics boost video model performance.
• Empirical results reveal that synthetic pre-training enhances generalization, outperforming natural video models on diverse out-of-distribution datasets.

Analyzing Video Representation Learning Through Synthetic Data

The paper "Learning Video Representations Without Natural Videos" by Xueyang Yu, Xinlei Chen, and Yossi Gandelsman investigates the potential of using synthetic videos combined with static images to pre-train models for video understanding tasks. This paper challenges the conventional reliance on natural videos, suggesting that carefully constructed synthetic datasets can achieve competitive performance in learning video representations.

The authors propose a sequence of progressively complex synthetic video datasets, each designed to incorporate additional features characteristic of natural videos, such as motion, acceleration, and shape transformations. VideoMAE, a state-of-the-art video model, serves as the framework for testing the efficacy of these datasets. The synthetic data's ability to close a significant portion of the performance gap typically observed when training solely on natural videos is tested across multiple action classification tasks including UCF101, HMDB51, and Kinetics-400.

Key Findings and Results

1. Progressive Dataset Complexity: The paper introduces a systematic progression of synthetic datasets starting from static circles and expanding to moving and transforming textured shapes. As the dataset complexity increases, so does the performance of the pre-trained models on downstream tasks. Interestingly, incorporating natural image crops into the synthetic datasets enables the model to either match or surpass the performance of models pre-trained on UCF101, a robust action recognition dataset.
2. Performance Metrics: Through quantitative analysis, the authors demonstrate that a VideoMAE model pre-trained on their synthetic datasets closes 97.2% of the classification accuracy gap on UCF101 compared to models trained from scratch and those pre-trained on UCF101 videos. Furthermore, the model outperforms UCF101 pre-trained models on 11 out of 14 out-of-distribution datasets of UCF101-P, highlighting the robustness of representations learned from synthetic data in varied conditions.
3. Dataset Property Analysis: The paper extends beyond mere performance metrics, exploring the intrinsic properties of the generated datasets. Metrics such as frame diversity, frame similarity to natural data, color distribution, and spectrum characteristics are correlated with downstream performance to guide future synthetic dataset designs.
4. Future Implications for Video Representation Learning: The implications of this research are considerable, suggesting a shift towards synthetic data for training video models, which offers a more controllable, transparent, and ethical alternative to the traditional data curation processes. The findings point towards a reduced dependency on large, often unwieldy datasets like Kinetics-400, making video representation learning more efficient.

Theoretical and Practical Implications

From a theoretical perspective, this research challenges assumptions about the necessity of natural video data for effective pre-training, positing instead that synthetic data can be engineered to include the essential properties for robust video understanding. Practically, this translates to more efficient data handling, reduced computational cost, and potential applications in areas where data privacy or availability is a concern.

The paper also postulates that the integration of static images can enhance generalization to out-of-distribution tasks, paving the way for new strategies in synthetic dataset construction. These insights offer a promising avenue for future research in self-supervised learning and synthetic data generation.

Overall, this research indicates a significant opportunity for further exploration into synthetic data as a viable, if not superior, alternative to natural video datasets in learning video representations. This shift can not only democratize data acquisition but also ensure the ethical utilization of data in AI systems.