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Video Correlation Learning (VCL), which aims to analyze the relationshipsbetween videos, has been widely studied and applied in various general videotasks. However, applying VCL to instructional videos is still quite challengingdue to their intrinsic procedural temporal structure. Specifically, proceduralknowledge is critical for accurate correlation analyses on instructionalvideos. Nevertheless, current procedure-learning methods heavily rely onstep-level annotations, which are costly and not scalable. To address thisproblem, we introduce a weakly supervised framework called CollaborativeProcedure Alignment (CPA) for procedure-aware correlation learning oninstructional videos. Our framework comprises two core modules: collaborativestep mining and frame-to-step alignment. The collaborative step mining moduleenables simultaneous and consistent step segmentation for paired videos,leveraging the semantic and temporal similarity between frames. Based on theidentified steps, the frame-to-step alignment module performs alignment betweenthe frames and steps across videos. The alignment result serves as ameasurement of the correlation distance between two videos. We instantiate ourframework in two distinct instructional video tasks: sequence verification andaction quality assessment. Extensive experiments validate the effectiveness ofour approach in providing accurate and interpretable correlation analyses forinstructional videos.

Collaborative Weakly Supervised Video Correlation Learning for Procedure-Aware Instructional Video Analysis