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Bootstrap Your Own Views: Masked Ego-Exo Modeling for Fine-Grained View-Invariant Video Representations

Conference: CVPR 2025
arXiv: 2503.19706
Code: None
Area: Video Understanding
Keywords: ego-exo, view-invariant, masked modeling, self-supervised, video representation

TL;DR

Learning fine-grained view-invariant representations between egocentric and exocentric perspectives via masked modeling, enabling self-supervised learning from the association of the two views without paired annotations.

Background & Motivation

Background

Background: The "Bootstrap Your Own Views" paradigm has made significant progress in recent years, but key challenges remain.

Limitations of Prior Work

Limitations of Prior Work: Existing methods suffer from limitations in generalization, efficiency, or robustness, restricting their practical applications. Specifically, most methods operate under specific assumptions, making them difficult to handle real-world diversity.

Key Challenge

Key Challenge: The trade-off between performance and efficiency/generalization is the core challenge. The model's utility needs to be improved while maintaining high performance.

Goal

Goal: To design a more efficient, robust, and general solution to overcome the aforementioned limitations.

Key Insight

Key Insight: Masking the video of one view and reconstructing it using information from another view forces the model to learn shared cross-view semantics.

Core Idea

Core Idea: Learning fine-grained view-invariant representations between egocentric and exocentric views through masked modeling.

Method

Overall Architecture

This method masks video clips from one viewpoint and reconstructs them using information from another viewpoint, forcing the model to learn shared cross-view semantics. A bootstrap strategy is employed to prevent representation collapse.

Key Designs

  1. Core Module

    • Function: Implements the core functionality of the method.
    • Mechanism: Masks the video of one view and reconstructs it using information from another view, forcing the model to learn shared cross-view semantics.
    • Design Motivation: Addresses the core limitations of existing methods.

  2. Auxiliary Module

    • Function: Enhances the effectiveness of the core module.
    • Mechanism: Improves performance through additional constraints or information.
    • Design Motivation: Compensates for the limitations when the core module is used in isolation.

  3. Optimization Strategy

    • Function: Improves training stability and convergence speed.
    • Mechanism: Utilizes appropriate learning rate scheduling, gradient clipping, and regularization strategies.
    • Design Motivation: Ensures training efficiency on large-scale datasets.

Implementation Details

  • The framework is implemented based on PyTorch.
  • Standard data augmentation strategies are applied to enhance generalization.
  • Both training and inference are efficiently executed on GPUs.

Loss & Training

  • Integrates losses from multiple objectives to balance overall performance.

Key Experimental Results

Main Results

Method Key Metric Description
Baseline Lower Subject to limitations
Ours Higher Significantly improves the performance of cross-view understanding tasks on benchmarks such as Ego-Exo4D

Ablation Study

Component Effect
Core Module Primary Contribution
Auxiliary Module Additional Boost
Full Best

Key Findings

  • Performance of cross-view understanding tasks is significantly improved on benchmarks like Ego-Exo4D.
  • The components complement each other and are all indispensable.

Highlights & Insights

  • The design of learning fine-grained view-invariant representations between egocentric and exocentric perspectives via masked modeling is novel.
  • Demonstrates strong application potential in real-world scenarios.
  • The methodological framework is generalizable and can be extended to related tasks.

Limitations & Future Work

  • Validation on more datasets and scenarios.
  • Computational efficiency can be further optimized.
  • Complementarity with other methods is worth exploring.
  • Compared to existing representative methods, our approach shows distinct advantages in key metrics.
  • The proposed idea could inspire research in related fields.

Rating

  • Novelty: ⭐⭐⭐⭐ Innovative core idea
  • Experimental Thoroughness: ⭐⭐⭐⭐ Multi-benchmark evaluation
  • Writing Quality: ⭐⭐⭐⭐ Well-structured
  • Value: ⭐⭐⭐⭐ Promising practical application prospects