Memory-augmented Dense Predictive Coding for Video Representation Learning (2008.01065v1)

Abstract: The objective of this paper is self-supervised learning from video, in particular for representations for action recognition. We make the following contributions: (i) We propose a new architecture and learning framework Memory-augmented Dense Predictive Coding (MemDPC) for the task. It is trained with a predictive attention mechanism over the set of compressed memories, such that any future states can always be constructed by a convex combination of the condense representations, allowing to make multiple hypotheses efficiently. (ii) We investigate visual-only self-supervised video representation learning from RGB frames, or from unsupervised optical flow, or both. (iii) We thoroughly evaluate the quality of learnt representation on four different downstream tasks: action recognition, video retrieval, learning with scarce annotations, and unintentional action classification. In all cases, we demonstrate state-of-the-art or comparable performance over other approaches with orders of magnitude fewer training data.

• The paper introduces the MDPC framework, integrating compressive memory with predictive attention to generate multiple future video state hypotheses.
• It demonstrates state-of-the-art performance, achieving 84% accuracy on UCF101 through visual-only inputs like RGB frames and optical flow.
• Extensive evaluations across four tasks highlight MDPC's potential in advancing self-supervised video representation without relying on multimodal data.

Memory-augmented Dense Predictive Coding for Video Representation Learning

The paper explores an innovative architecture and learning framework, termed Memory-augmented Dense Predictive Coding (MDPC), for self-supervised video representation learning. It specifically addresses video-based self-supervised learning solely from the visual stream, aimed at improving action recognition.

Key Contributions

The authors of the paper outline several major contributions:

1. Novel Architecture: The introduction of the MDPC framework is pivotal. This architecture leverages predictive attention mechanisms and compressive memory to efficiently construct future states as combinations of learned representations. Such design addresses the challenge of creating multiple hypotheses about dynamic sequences in videos.
2. Self-supervised Learning Modalities: The work extends the exploration of video representation learning through visual-only inputs, including RGB frames and unsupervised optical flow components.
3. Thorough Evaluation: The quality of representations acquired with the new structure is extensively evaluated across four distinct downstream tasks: action recognition, video retrieval, learning with scarce annotations, and unintentional action classification, demonstrating state-of-the-art or comparable performance.

Methodological Insights

MDPC Architecture: The architecture innovatively involves a compressive memory module that aids in anticipating multiple future states within video sequences. This is achieved via a predictive attention mechanism that draws connections between compressed memory slots and the observed data, effectively managing the inherent ambiguity in multiple future predictions.

Contrastive Learning: Following contrastive predictive learning paradigms, MDPC trains on the principle of maximizing agreement between predictions and actual observations, amid distractors, which has shown meaningful progress in self-supervised learning frameworks.

Strong Numerical Results

The architecture's performance was evaluated with rigorous benchmarks and demonstrated robust outcomes across several datasets, outperforming previously established methods that relied on significantly larger datasets or multimodal inputs.

• The MDPC framework, particularly in a bi-directional form and utilizing both RGB and optical flow data, achieved an impressive 84% accuracy on UCF101, an improvement over various prior designs without relying on additional modalities.
• On the Kinetics400 dataset with video-only input, the performance was consistent, maintaining competitive results with models that exploit multiple sensory inputs.

Implications and Future Speculations

The findings provide compelling evidence of the effectiveness of memory-augmented approaches in handling video sequence ambiguities and improving the quality of video representations in self-supervised learning contexts. The implications here could steer future research towards more efficient architectures facilitating quality video analysis without reliance on extensive datasets or multimodal input streams.

Practical implications of this research include advancements in autonomous video content analysis systems and enhancing capabilities in domains where large-scale labeled data is unavailable. Theoretically, it contributes to a deeper understanding of memory mechanisms in machine learning that can enable the development of systems mimicking aspects of human predictive capabilities.

Looking ahead, future explorations could focus on extending this architecture’s stackability or integration with more advanced memory systems. Additionally, leveraging this framework to explore even more efficient representation learning on smaller-scale devices or lower-resource settings presents a promising frontier.