Self-Supervised Transformer for Instructional Video Step Localization
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Solution Overview
Problem
Existing key-step localization techniques in instructional videos are error-prone, time-consuming, and lack scalability, particularly when dealing with large datasets, as they rely on human annotations and are inefficient in filtering out uninformative content.
Innovation Solution
A self-supervised transformer model is used to extract step slots from instructional videos and match them with video segments using an order-aware sequence-to-sequence alignment model, enabling the identification and localization of key steps without prior knowledge of the video content, thus automating the process and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotations are used for key-step localization, then accuracy may be improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system performs self-supervised learning where the model automatically generates its own training signals from the video content without requiring external human annotations. The transformer model extracts step slots and aligns them with video segments autonomously, eliminating the need for time-consuming manual labeling while maintaining localization accuracy
Solution Approach 2:
The patent replaces the mechanical process of human annotation with an automated computational system. The order-aware sequence-to-sequence alignment model and transformer architecture substitute human reviewers with algorithmic processing, achieving scalable key-step localization across large datasets without proportional increases in time investment
2Measurement precision
If human annotations are used for key-step localization, then accuracy may be improved, but scalability to large datasets deteriorates
Solution Approach 1:
The self-supervised transformer model processes videos independently without requiring human annotators for each dataset. The system generates step slots and performs alignment automatically, enabling linear scaling to large datasets while maintaining consistent localization accuracy across different video collections
Solution Approach 2:
The patent creates a universal system that can handle diverse instructional videos across multiple domains (cooking, furniture assembly, tire changing, etc.) using the same transformer architecture and alignment model. This multi-functional approach allows scalable processing of large, heterogeneous datasets without requiring domain-specific annotation expertise
3Device complexity
If traditional methods are used for key-step localization, then implementation may be simpler, but ability to filter uninformative content deteriorates
Solution Approach 1:
The patent replaces simple temporal alignment methods with sophisticated transformer-based semantic understanding. The model captures contextual relationships between step slots and video segments, enabling effective filtering of uninformative content like advertisements and title frames while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The system changes from basic temporal markers to rich semantic representations using transformer embeddings. By transforming the representation parameters of video and text into high-dimensional semantic spaces, the model achieves superior content filtering capability while keeping the overall method complexity controlled through efficient attention mechanisms
Data Source
AI summary
The present disclosure provides methods, apparatuses, and computer-readable media for step discovery and localization in an instructional video. In some embodiments, the method includes extracting, from the instructional video using a transformer model, a plurality of step slots corresponding to a plurality of procedure steps depicted in the instructional video, matching, using an order-aware sequence-to-sequence alignment model, a plurality of video segments of the instructional video to the plurality of step slots, generating a temporally-ordered plurality of video segments from the plurality of video segments, receiving a user query requesting a procedure step, selecting, from the plurality of video segments of the instructional video, a corresponding video segment corresponding to the requested procedure step, and providing, in response to the user query, the corresponding video segment and the matching textual step description of the corresponding video segment.