A Weakly Supervised Collaborative Procedure Alignment Framework for Procedural Video Analysis

Information Science Electronic Information Artificial Intelligence Computer Science
video understanding correlation learning instructional videos procedure alignment weak supervision feature matching

Achieving Procedure-Aware Instructional Video Correlation Learning Under Weak Supervision: Summary and Evaluation

In recent years, instructional videos have garnered significant attention due to their goal-driven characteristics and intrinsic connections to human learning processes. Compared to general videos, instructional videos contain multiple fine-grained steps with varying durations and temporal locations, resulting in more complex procedural structures. This study proposes a novel framework, Collaborative Procedure Alignment (CPA), under weak supervision to facilitate procedure-aware correlation learning for instructional videos. The framework eliminates the need for costly step-level annotations by collaboratively extracting procedural information and quantifying video correlations, significantly improving the efficiency and effectiveness of instructional video correlation learning.

Research Context and Problem Statement

Instructional video correlation learning (VCL) aims to analyze and quantify relationships between videos through comparative learning. Traditional VCL approaches are primarily applied to general videos, which typically exhibit unified semantic and temporal information. Consequently, these methods focus on coarse-grained comparisons. However, the complex procedural structure of instructional videos renders traditional VCL methods ineffective.

Current procedural learning methods for instructional videos heavily depend on detailed step-level annotations, which include semantic labels and temporal boundaries of steps. These annotations are not only expensive but also non-scalable. Therefore, learning intrinsic procedural knowledge from instructional videos without step-level annotations has emerged as a significant challenge.

To address this issue, the study introduces a weakly supervised CPA framework designed to extract step-level information and quantify procedural consistency based on the inherent correlations between paired instructional videos.

Study Source and Author Background

This study, conducted by researchers from Shanghai Jiao Tong University’s Department of Electronic Engineering in collaboration with Lenovo Research and the Chinese Academy of Electronics and Information Technology, was published in the International Journal of Computer Vision in 2024. The research was supported by the National Natural Science Foundation of China (No. U21B2013).

Framework Design and Workflow

1. Framework Design

The CPA framework consists of two core modules: 1. Collaborative Step Mining (CSM): - Utilizes semantic similarity and temporal continuity of video frames for step segmentation. - Employs dynamic programming to extract block-diagonal structures in relational matrices, ensuring accurate and consistent step segmentation. 2. Frame-to-Step Alignment (FSA): - Computes alignment probabilities between one video’s frame-level features and another video’s step-level features to quantify procedural consistency.

These modules work synergistically, enhancing each other: the CSM module provides precise step-level information to support FSA alignment, while FSA feedback optimizes CSM’s step segmentation.

2. Data Processing and Algorithm Implementation

The CPA framework follows these steps: 1. Encode input video frames to generate frame-level feature representations. 2. Use the CSM module to extract step boundaries and calculate procedural consistency. 3. Combine frame-level and step-level features for correlation computation.

Dynamic programming significantly enhances segmentation efficiency and accuracy, while probabilistic alignment optimizes cross-video verification.

Experiments and Analysis

1. Experiment Setup

The study evaluates CPA across various instructional video tasks, including sequence verification, few-shot action recognition, temporal action segmentation, and action quality assessment. Comparisons with state-of-the-art methods validate CPA’s superior performance.

2. Key Task Performances

Sequence Verification

This task identifies whether two instructional videos follow the same procedure. On the Chemical Sequence Verification (CSV) dataset, CPA outperformed competitors in AUC and WDR metrics, demonstrating strong procedural consistency evaluation capabilities.

Few-Shot Action Recognition

CPA significantly improved classification accuracy in scenarios with limited training samples. On CSV-FSL and Diving-FSL datasets, CPA surpassed all competitors in both 1-shot and 5-shot settings.

Temporal Action Segmentation

On the Breakfast dataset, CPA achieved competitive mean-over-frame (MoF) accuracy under unsupervised settings, validating the effectiveness of its step mining module.

Action Quality Assessment

By integrating CPA with the TSA method, the framework achieved state-of-the-art results on the FineDiving dataset without requiring step-level annotations. CPA’s flexible step segmentation enabled superior performance in quality assessment tasks.

Innovative Extensions

1. Flexible Procedure Matching

CPA was extended to flexible procedure matching tasks, allowing users to set customizable thresholds for procedural similarity. Experimental results showed CPA’s superior classification accuracy compared to alternative methods across various thresholds.

2. Step Combination Retrieval

CPA was also applied to retrieve videos containing specific step combinations and their temporal locations. This feature showcases CPA’s potential in applications requiring step-specific monitoring and educational feedback.

Significance and Applications

1. Scientific Value

CPA introduces a novel weakly supervised framework for instructional video correlation learning, advancing procedure-aware video understanding.

2. Practical Applications

  • Education and Training: Assists in verifying instructional video procedures and alerting incorrect operations.
  • Sports Evaluation: Enhances scoring systems in sports like diving and gymnastics.
  • Industrial Monitoring: Supports step combination detection for industrial processes and safety monitoring.

3. Methodological Strengths

  • Innovation: First to introduce a collaborative weak supervision framework to instructional VCL.
  • Efficiency: Reduces computational complexity through dynamic programming and probabilistic alignment.
  • Flexibility: Adapts to diverse video understanding tasks and supports advanced functionalities.

Conclusion

The CPA framework achieves efficient and accurate instructional video correlation learning through collaborative step mining and frame-to-step alignment. Its exceptional performance across various tasks and potential for advanced applications highlight its value in instructional video analysis and broader video understanding research.