VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning (2106.11250v1)

Abstract: Video understanding relies on perceiving the global content and modeling its internal connections (e.g., causality, movement, and spatio-temporal correspondence). To learn these interactions, we apply a mask-then-predict pre-training task on discretized video tokens generated via VQ-VAE. Unlike language, where the text tokens are more independent, neighboring video tokens typically have strong correlations (e.g., consecutive video frames usually look very similar), and hence uniformly masking individual tokens will make the task too trivial to learn useful representations. To deal with this issue, we propose a block-wise masking strategy where we mask neighboring video tokens in both spatial and temporal domains. We also add an augmentation-free contrastive learning method to further capture the global content by predicting whether the video clips are sampled from the same video. We pre-train our model on uncurated videos and show that our pre-trained model can reach state-of-the-art results on several video understanding datasets (e.g., SSV2, Diving48). Lastly, we provide detailed analyses on model scalability and pre-training method design. Code is released at https://github.com/airsplay/vimpac.

Summary of "VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning"

The paper entitled "VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning" by Hao Tan et al. presents a novel approach to self-supervised video understanding that leverages both masked token prediction and contrastive learning. This work is situated within the context of advancing video representation learning, where traditional methods typically bifurcate into image-based contrastive learning and language-based token prediction paradigms. VIMPAC navigates these domains by integrating their strengths into a cohesive framework specifically tailored for video data.

Technical Contributions

1. Block-wise Masking Strategy: The core proposition of the paper is incorporating a mask-then-predict task tailored to the unique characteristics of video data. Due to strong temporal correlations in video data, a naive application of uniform token masking—as seen in text-based models—would lead to trivial learning since consecutive frames are often similar. To counter this, the authors propose a block-wise masking strategy where contiguous spatial-temporal regions are masked, thereby requiring models to learn long-range dependencies for effective reconstruction.
2. Quantized Video Tokens with VQ-VAE: The paper utilizes Vector Quantized-Variational Autoencoder (VQ-VAE) for encoding video frames into discrete token representations, significantly reducing the dimensionality and emphasizing core spatial-temporal structures over pixel-level details. This approach mitigates the computational expense typical of larger video datasets.
3. Augmentation-free Contrastive Learning: Departing from traditional augmentation-heavy contrastive methods, VIMPAC adapts a novel approach by leveraging video samples encoded from the same video as positive examples, which enriches the model's ability to capture global content without extensive augmentation. Remarkably, VIMPAC effectively contrasts video segments potentially separated by long temporal distances, unlike previous methods restricted to closer samples.

Empirical Evaluation and Results

VIMPAC's efficacy is demonstrated through performance evaluations on several benchmarks, including SSV2, Diving48, UCF101, HMDB51, and Kinetics-400. Notably, it achieves state-of-the-art results on temporally-intensive datasets such as SSV2 and Diving48, underscoring the model’s proficiency in handling such data. In contrast, performance on spatially-intensive datasets is competitive though slightly less leading, likely due to the inherent information loss in the VQ-VAE discretization process. This highlights a potential area for future enhancement in preserving finer spatial details while utilizing quantization for sparsity.

Discussion and Future Directions

The implications of VIMPAC's methodology extend beyond immediate performance gains; they challenge the established norms in contrastive video learning by demonstrating that augmentation is not a strict necessity for effective representation learning. Future investigations might probe deeper into optimizing the tokenization process to balance detail preservation with computational efficiency fully.

Moreover, the blend of masked token prediction with contrastive learning opens avenues to refine both objectives, potentially through dynamic weighting or adaptive strategies that highlight specific tasks based on the type of video content being processed.

In conclusion, VIMPAC represents a sophisticated step in video representation learning, showcasing how methodologies reserved for static images or linear text can be adroitly adapted and enhanced for the complex and dynamic nature of video data. Such advancements are likely to spur further research into hybrid learning models that adeptly synthesize local and global context understanding in visual datasets.