EgoInteract is a new simulator for generating synthetic egocentric videos with precise control over camera, body, hand, and object motions, producing a dataset that improves model performance on real-world benchmarks for temporal action segmentation, next-active object detection, interaction Anticip
Temporal convolutional networks for action segmentation and detection
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Introduces SIA-RAPN benchmark of 50 clinical videos with 12 fine-grained renorrhaphy action labels and evaluates four temporal segmentation models, with DiffAct leading on most metrics.
An MCMC framework enforces empirical transition laws on GAN outputs to reduce temporal drift in synthetic multivariate time series.
citing papers explorer
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EgoInteract: Synthetic Egocentric Videos Generation for Interaction Understanding and Anticipation
EgoInteract is a new simulator for generating synthetic egocentric videos with precise control over camera, body, hand, and object motions, producing a dataset that improves model performance on real-world benchmarks for temporal action segmentation, next-active object detection, interaction Anticip
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Fine-Grained Action Segmentation for Renorrhaphy in Robot-Assisted Partial Nephrectomy
Introduces SIA-RAPN benchmark of 50 clinical videos with 12 fine-grained renorrhaphy action labels and evaluates four temporal segmentation models, with DiffAct leading on most metrics.
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Preserving Temporal Dynamics in Time Series Generation
An MCMC framework enforces empirical transition laws on GAN outputs to reduce temporal drift in synthetic multivariate time series.