Visual Representation Learning with Stochastic Frame Prediction (2406.07398v2)

Abstract: Self-supervised learning of image representations by predicting future frames is a promising direction but still remains a challenge. This is because of the under-determined nature of frame prediction; multiple potential futures can arise from a single current frame. To tackle this challenge, in this paper, we revisit the idea of stochastic video generation that learns to capture uncertainty in frame prediction and explore its effectiveness for representation learning. Specifically, we design a framework that trains a stochastic frame prediction model to learn temporal information between frames. Moreover, to learn dense information within each frame, we introduce an auxiliary masked image modeling objective along with a shared decoder architecture. We find this architecture allows for combining both objectives in a synergistic and compute-efficient manner. We demonstrate the effectiveness of our framework on a variety of tasks from video label propagation and vision-based robot learning domains, such as video segmentation, pose tracking, vision-based robotic locomotion, and manipulation tasks. Code is available on the project webpage: https://sites.google.com/view/2024rsp.

• The paper introduces a novel stochastic frame prediction model (RSP) that captures multiple potential future frames for enhanced visual representation.
• It integrates a time-dependent prior with auxiliary masked image modeling to reinforce both temporal and spatial feature learning.
• Empirical results show significant improvements in tasks like robotic manipulation (36% success rate) and video label propagation benchmarks.

Visual Representation Learning with Stochastic Frame Prediction

The paper explores the domain of self-supervised learning by addressing the challenge of visual representation learning through stochastic frame prediction in videos. The primary motivation lies in the inherently under-determined nature of frame prediction, where a single current frame can lead to multiple possible future frames. This poses a challenge for the conventional deterministic models that struggle to capture the multivariate distribution of future possibilities. The proposed approach revisits the concept of stochastic video generation and adapts it to the field of representation learning.

Methodological Framework

The authors introduce a novel framework named Representation learning with Stochastic frame Prediction (RSP). At the core, this framework relies on a stochastic frame prediction model designed to discern temporal information across frames in video sequences. The methodology involves:

• Stochastic Frame Prediction: Leveraging the stochastic nature of future frame possibilities, the model designs a probabilistic approach to anticipate future frames from a given current state. This involves the use of a time-dependent prior that captures the uncertainty inherent in video sequences.
• Auxiliary Masked Image Modeling: To enrich the spatial representation within each frame, the framework incorporates a masked image modeling objective. The inclusion of a shared decoder architecture facilitates the integration of this auxiliary task without substantial computational burdens, allowing for mutual reinforcement between the tasks.

Numerical Evaluation and Empirical Insights

The proposed method underwent extensive empirical evaluations across diverse tasks, such as video label propagation and vision-based robotic tasks. RSP demonstrates superior performance, highlighting the efficacy of stochastic prediction models over deterministic counterparts, particularly in complex real-world environments.

Key results indicated:

• Robotic Manipulation Tasks: The method showed a notable 36.0% average success rate, significantly outperforming the baseline deterministic methods, which hovered around a 13.5% success rate, thereby showcasing its utility in vision-based robot learning.
• Video Label Propagation Tasks: The model's performance was tested on benchmarks like DAVIS and JHMDB, where it delivered competitive results, underscoring the potential of stochastic elements in capturing temporal dynamics more effectively than existing methods.

Implications and Future Directions

Theoretically, the integration of stochastic predictions furnishes richer representations by capturing the variance in potential future states. Practically, this model opens pathways for more robust applications in fields where understanding dynamics is crucial, such as autonomous driving and robotic systems.

Future developments could leverage larger-scale datasets and further refine the stochastic models by integrating more expressive priors. Additionally, expanding the model to handle multi-frame inputs could enhance the temporal understanding further. While concerns of computational efficiency remain, the work paves the way for more nuanced models in AI, strengthening the bridge between generative modeling and representation learning.

In sum, the research advances the field by proposing a viable alternative to the deterministic frame prediction, emphasizing the role of stochastic methodologies in understanding and modeling complex temporal systems.