ArticleProceedings. IEEE International Conference on Computer Vision2015
Unsupervised Synchrony Discovery in Human Interaction.
Article in Proceedings. IEEE International Conference on Computer Vision, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Application of deep learning in teeth identification tasks on panoramic radiographs.Dento maxillo facial radiology · 2022Review
- Assessing Sensorimotor Synchronisation in Toddlers Using the Lookit Online Experiment Platform and Automated Movement Extraction.Frontiers in psychology · 2022Article
- Discovering Synchronized Subsets of Sequences: A Large Scale Solution.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2020Article
- A Branch-and-Bound Framework for Unsupervised Common Event Discovery.International journal of computer vision · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
People are inherently social. Social interaction plays an important and natural role in human behavior. Most computational methods focus on individuals alone rather than in social context. They also require labelled training data. We present an unsupervised approach to discover interpersonal synchrony, referred as to two or more persons preforming common actions in overlapping video frames or segments. For computational efficiency, we develop a branch-and-bound (B&B) approach that affords exhaustive search while guaranteeing a globally optimal solution. The proposed method is entirely general. It takes from two or more videos any multi-dimensional signal that can be represented as a histogram. We derive three novel bounding functions and provide efficient extensions, including multi-synchrony detection and accelerated search, using a warm-start strategy and parallelism. We evaluate the effectiveness of our approach in multiple databases, including human actions using the CMU Mocap dataset [1], spontaneous facial behaviors using group-formation task dataset [37] and parent-infant interaction dataset [28].
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.