Towards Automatic Learning of Procedures from Web Instructional Videos (1703.09788v3)

Abstract: The potential for agents, whether embodied or software, to learn by observing other agents performing procedures involving objects and actions is rich. Current research on automatic procedure learning heavily relies on action labels or video subtitles, even during the evaluation phase, which makes them infeasible in real-world scenarios. This leads to our question: can the human-consensus structure of a procedure be learned from a large set of long, unconstrained videos (e.g., instructional videos from YouTube) with only visual evidence? To answer this question, we introduce the problem of procedure segmentation--to segment a video procedure into category-independent procedure segments. Given that no large-scale dataset is available for this problem, we collect a large-scale procedure segmentation dataset with procedure segments temporally localized and described; we use cooking videos and name the dataset YouCook2. We propose a segment-level recurrent network for generating procedure segments by modeling the dependencies across segments. The generated segments can be used as pre-processing for other tasks, such as dense video captioning and event parsing. We show in our experiments that the proposed model outperforms competitive baselines in procedure segmentation.

• The paper introduces a novel Procedure Segmentation Network (ProcNets) that automatically delineates procedural steps in instructional videos.
• It leverages context-aware encoding, temporal anchors, and sequential prediction to effectively segment long, unconstrained videos.
• The framework, evaluated on the extensive YouCook2 dataset with Jaccard and mean IoU metrics, outperforms traditional baselines.

Towards Automatic Learning of Procedures from Web Instructional Videos

The paper "Towards Automatic Learning of Procedures from Web Instructional Videos" presents a comprehensive paper on automatically segmenting instructional videos into distinct procedure segments. The authors introduce the YouCook2 dataset to address the scarcity of large datasets suitable for this task and propose a novel framework, Procedure Segmentation Networks (ProcNets), to tackle the problem.

Core Problem Definition

The authors define procedure segmentation, aiming to partition a video into independent procedure segments without relying on action labels or video subtitles. The complexity of this task is heightened in long, unconstrained videos where conventional action recognition models fall short.

Dataset: YouCook2

To facilitate research in procedure segmentation, the authors introduce the YouCook2 dataset. This dataset consists of 2000 cooking videos from 89 recipes, with temporal boundaries that denote procedure steps. It surpasses existing datasets in scale and annotation detail, marking a significant contribution to the community.

Methodology: Procedure Segmentation Networks

ProcNets are designed to learn the structure of procedures through three main modules:

1. Context-Aware Video Encoding: Utilizes bi-directional LSTM to incorporate temporal context into frame-wise features derived from ResNet.
2. Procedure Segment Proposal: Employs a set of temporal anchors and offset learning to propose potential segment boundaries. This proposal mechanism distinguishes between procedure segments and non-procedure content.
3. Sequential Prediction: Leverages LSTM to model dependencies among proposed segments, outputting a sequence that adheres to human-consensus procedural structure.

Evaluation and Results

The paper demonstrates that ProcNets outperform competitive baselines, such as vsLSTM and SCNN-prop. Using metrics like Jaccard and mean IoU, ProcNets effectively capture procedure segments with higher accuracy. ProcNets-LSTM, in particular, shows robust performance in both proposal recall and localization when compared to both traditional and sequential prediction-based methods.

Implications and Future Directions

The research offers valuable insights into procedure segmentation, essential for tasks like dense video captioning and event parsing. The ProcNet framework's segment-level approach can inspire future developments in AI that require an understanding of temporal dependencies in video data. Moreover, the proposed method's adaptability to unseen video content further highlights its potential application.

The paper sets a precedent for expanding research into weakly supervised learning scenarios, where textual alignment with video content is minimal. Potential future work includes refining segmentation techniques and exploring diverse application domains beyond instructional videos.

By contributing a substantial dataset and an innovative segmentation model, this work provides a foundational platform for future advancements in video content analysis and AI-driven procedural understanding.