Step Differences in Instructional Video
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Abstract (arXiv)
Comparing a user video to a reference how-to video is a key requirement for AR/VR technology delivering personalized assistance tailored to the user's progress. However, current approaches for language-based assistance can only answer questions about a single video. We propose an approach that first automatically generates large amounts of visual instruction tuning data involving pairs of videos from HowTo100M by leveraging existing step annotations and accompanying narrations, and then trains a video-conditioned language model to jointly reason across multiple raw videos. Our model achieves state-of-the-art performance at identifying differences between video pairs and ranking videos based on the severity of these differences, and shows promising ability to perform general reasoning over multiple videos. Project page: https://github.com/facebookresearch/stepdiff
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 54 of its 72 references that are in the map. First posted on arXiv 24 Apr 2024.
Builds on
54 of 72 references.
References · 54
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38 more
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50 most cited of 54 All 54 (JSON)
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Citing papers per year: 2024: 2; 2025: 5; 2026: 4.
11 papers.
- 2025Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)cited by 23
- 2024ExpertAF: Expert Actionable Feedback from Videocited by 17
- 2025Video Action Differencingcited by 14
- 2025ConViS-Bench: Estimating Video Similarity Through Semantic Conceptscited by 4
- 2025Learning Skill-Attributes for Transferable Assessment in Videocited by 4
- 2024It's Just Another Day: Unique Video Captioning by Discriminative Promptingcited by 3
- 2025Stitch-a-Demo: Video Demonstrations from Multistep Descriptionscited by 1
- 2026SportSkills: Physical Skill Learning from Sports Instructional Videoscited by 1
- 2026Let's Split Up: Zero-Shot Classifier Edits for Fine-Grained Video Understandingcited by 0
- 2026Anomalous Frame Detection by Grouping Frame Similarities between Two Videos Computed by Vision-Language Model to Extract Expert Workers' Unique Actionscited by 0
- 2026Anomalous Frame Detection Using VLM-Based Description Comparison for Extracting Expert-Specific Actions and Contextual Decision-Making Scenes with Intra-Video Self-Similaritycited by 0
- 2026Anomalous Frame Detection Using VLM-Based Description Comparison for Extracting Expert-Specific Actions and Contextual Decision-Making Scenes with Intra-Video Self-Similarity12 Jul 2026
- 2026Anomalous Frame Detection by Grouping Frame Similarities between Two Videos Computed by Vision-Language Model to Extract Expert Workers' Unique Actions12 Jul 2026
- 2026SportSkills: Physical Skill Learning from Sports Instructional Videos26 Mar 2026
- 2026Let's Split Up: Zero-Shot Classifier Edits for Fine-Grained Video Understanding18 Feb 2026
- 2025Learning Skill-Attributes for Transferable Assessment in Video17 Nov 2025
- 2025ConViS-Bench: Estimating Video Similarity Through Semantic Concepts23 Sep 2025
- 2025Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)4 Apr 2025
- 2025Stitch-a-Demo: Video Demonstrations from Multistep Descriptions18 Mar 2025
- 2025Video Action Differencing10 Mar 2025
- 2024It's Just Another Day: Unique Video Captioning by Discriminative Prompting15 Oct 2024
- 2024ExpertAF: Expert Actionable Feedback from Video1 Aug 2024