COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis (1903.02874v1)

Abstract: There are substantial instructional videos on the Internet, which enables us to acquire knowledge for completing various tasks. However, most existing datasets for instructional video analysis have the limitations in diversity and scale,which makes them far from many real-world applications where more diverse activities occur. Moreover, it still remains a great challenge to organize and harness such data. To address these problems, we introduce a large-scale dataset called "COIN" for COmprehensive INstructional video analysis. Organized with a hierarchical structure, the COIN dataset contains 11,827 videos of 180 tasks in 12 domains (e.g., vehicles, gadgets, etc.) related to our daily life. With a new developed toolbox, all the videos are annotated effectively with a series of step descriptions and the corresponding temporal boundaries. Furthermore, we propose a simple yet effective method to capture the dependencies among different steps, which can be easily plugged into conventional proposal-based action detection methods for localizing important steps in instructional videos. In order to provide a benchmark for instructional video analysis, we evaluate plenty of approaches on the COIN dataset under different evaluation criteria. We expect the introduction of the COIN dataset will promote the future in-depth research on instructional video analysis for the community.

• The paper introduces the COIN dataset, comprising 11,827 videos across 180 tasks in 12 domains to address limitations in instructional video analysis.
• It employs a three-level hierarchical annotation strategy (domain, task, step) and a tailored toolbox to precisely label complex video sequences.
• Evaluations reveal significant challenges in step localization, highlighting the need for advanced models to interpret intricate instructional content.

A Comprehensive Overview of the COIN Dataset for Instructional Video Analysis

The paper presents the COIN dataset, a significant contribution to the field of instructional video analysis. With the growth of video data, particularly instructional content, the paper identifies the limitations of existing datasets in terms of diversity and scale, prompting the development of COIN. This dataset is noteworthy for its scale, comprising 11,827 videos of 180 tasks across 12 daily life domains. This hierarchical organization addresses the need for a comprehensive dataset that reflects the complexity of real-world instructional activities.

The COIN dataset is distinct due to its three-level structure: domain, task, and steps. Each task within a domain is broken down into specific steps, offering detailed insights into task execution. For example, a task in the domain "vehicles" could involve sequential steps such as "unscrew the screws" and "jack up the car". This fine-grained annotation is crucial for developing and testing models that understand complex sequences in video content.

A critical innovation presented in the paper is the annotation toolbox, designed to improve the efficiency and accuracy of dataset labeling. By employing both frame and video modes, the toolbox facilitates the precise identification and annotation of steps within long and multifaceted video sequences. This methodically annotated dataset provides a robust benchmark for developing algorithms capable of parsing instructional content.

Another highlight of the paper is a proposed method to enhance step localization through task consistency. The method leverages the intrinsic dependencies within instructional videos, ensuring that steps correlate with the overarching task they contribute to. This bottom-up and top-down approach refines action detection by first predicting task labels from aggregated step predictions and then enforcing step-task consistency, thereby improving localization performance.

The paper evaluates multiple state-of-the-art methods for step localization and action segmentation on COIN. Notably, the results reveal significant challenges due to the dataset's nuanced and complex structure, reflected in the relatively low performance metrics even for advanced methods. These challenges underscore the need for continued innovation in instructional video analysis methodologies.

In relation to other datasets, COIN offers a broader scope and larger scale. It surpasses the YouCook2 dataset in proposal localization tasks, demonstrating COIN's utility in testing advanced video analysis algorithms. Furthermore, the detailed task taxonomy and extensive annotation enable more profound exploration of action recognition and segmentation strategies, revealing new research avenues.

The implications of this work are far-reaching, particularly in advancing machine understanding of instructional content through video. The COIN dataset sets a new benchmark for diversity and complexity, propelling future research in temporal action localization and hierarchical task understanding. In the evolving landscape of AI and computer vision, COIN offers a pivotal resource for developing systems capable of accurately interpreting and executing instructional content, with potential applications across education, augmented reality, and automated assistance systems.