Video Representation Learning with Joint-Embedding Predictive Architectures (2412.10925v1)

Abstract: Video representation learning is an increasingly important topic in machine learning research. We present Video JEPA with Variance-Covariance Regularization (VJ-VCR): a joint-embedding predictive architecture for self-supervised video representation learning that employs variance and covariance regularization to avoid representation collapse. We show that hidden representations from our VJ-VCR contain abstract, high-level information about the input data. Specifically, they outperform representations obtained from a generative baseline on downstream tasks that require understanding of the underlying dynamics of moving objects in the videos. Additionally, we explore different ways to incorporate latent variables into the VJ-VCR framework that capture information about uncertainty in the future in non-deterministic settings.

• The paper introduces a novel self-supervised video learning method using JEPA and variance-covariance regularization to prevent representation collapse.
• It shifts the focus from pixel-level reconstruction to abstract feature prediction, effectively capturing temporal and semantic dynamics.
• Experimental results on datasets like MovingMNIST and CATER demonstrate improved speed probing and action recognition compared to generative models.

Video Representation Learning with Joint-Embedding Predictive Architectures

The paper "Video Representation Learning with Joint-Embedding Predictive Architectures" presents a novel approach to self-supervised video representation learning using a joint-embedding predictive architecture (JEPA), specifically the Video JEPA with Variance-Covariance Regularization (VJ-VCR). This architecture is designed to address key challenges in video representation by focusing on high-level abstraction rather than low-level pixel details, effectively enhancing the model's ability to capture the dynamics of moving objects within video data.

Background and Motivation

The exponential growth of video data necessitates efficient and effective methods for video representation learning. Traditional supervised methods require large quantities of labeled data, which are often expensive and time-consuming to obtain. Self-supervised learning (SSL) provides an alternative by leveraging the inherent structure of video data, enabling models to learn representations from the data itself without external annotations. The present work seeks to improve upon generative models in SSL, which typically make pixel-level predictions and consequently have to deal extensively with reconstructing low-level details. By contrast, JEPA shifts the prediction task into the feature space, thereby prioritizing the capture of high-level temporal and semantic dynamics.

Methodology

The proposed VJ-VCR model integrates JEPA principles with variance-covariance regularization to avoid representation collapse—a common issue where models devolve into mapping all inputs to the same representation, thus losing the ability to discern differences between them. In VJ-VCR, variance is promoted within each hidden feature component, and covariance is minimized across components, ensuring diverse and rich representations.

1. Joint-Embedding Predictive Architecture: Unlike typical generative models, VJ-VCR predicts future frames not by recreating pixel values but by estimating their abstract representations. This design inherently reduces computational costs and allows the model to focus on learning significant patterns and temporal information.
2. Variance-Covariance Regularization: Employed to maintain representation diversity, this involves two metrics—variance, which ensures that representations have sufficient spread, and covariance, which de-correlates learned features to foster independent, informative dimensions.
3. Latent Variables: These are utilized to account for the inherent uncertainty and non-determinism present in real-world future predictions. Latent variables enable the model to capture potential variations in video sequences that cannot be solely inferred from past frames.

Experimental Setup and Results

The efficacy of VJ-VCR was tested against generative baseline models using both deterministic and non-deterministic datasets such as MovingMNIST, CLEVRER, and CATER. Across various datasets:

• Speed Probing: VJ-VCR demonstrated superior performance in abstract representation tasks like speed probing, indicating its capability to encode dynamic information more effectively than its generative counterparts.
• Action Recognition: For non-deterministic datasets such as CATER, VJ-VCR models outperformed generative baselines in action recognition tasks, illustrating the utility of high-order representations in understanding and predicting complex video interactions.
• Information Content: Analyses through singular value decomposition confirmed that VJ-VCR effectively mitigates dimensional collapse, ensuring richer representations compared to generative-only models.

Implications and Future Directions

By advancing video representation learning through JEPA and addressing representation collapse with variance-covariance regularization, this research contributes significantly to the field of computer vision and machine learning. The results suggest that focusing on the predictive capabilities within an abstract feature space rather than pixel-level reconstruction can enhance model performance on complex, dynamic tasks.

Going forward, the potential for extending VJ-VCR to larger, more varied datasets remains a promising avenue for future research. Additionally, exploring the integration of VJ-VCR with advanced neural architectures like Vision Transformers could yield further improvements in model efficiency and representation richness. Enhanced exploration into the role of latent variables in capturing stochastic elements of video data also offers a fertile area for development.