InvVis: Large-Scale Data Embedding for Invertible Visualization

Ye, Huayuan; Li, Chenhui; Li, Yang; Wang, Changbo

doi:10.1109/tvcg.2023.3326597

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2024

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Cited by 3 publications

(3 citation statements)

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“…Feature representation plays an important role in various domains, such as dimensionality reduction [27,36,37], anomaly detection [14,24], data steganography [73,75], etc. Early approaches are commonly based on traditional machine learning methods.…”

Section: Spatial Feature Representationmentioning

confidence: 99%

SalienTime: User-driven Selection of Salient Time Steps for Large-Scale Geospatial Data Visualization

Chen,

Huang,

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Section: Spatial Feature Representationmentioning

confidence: 99%

SalienTime: User-driven Selection of Salient Time Steps for Large-Scale Geospatial Data Visualization

Chen,

Huang,

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…The resulting latent space has similar visual characteristics to the original image space, and the model can decode the hidden data. Ye et al (2023) extended the steganography method using a flow-based model to hide more data in visualizations.…”

Section: Latent Space Representation In Visualizationmentioning

confidence: 99%

“…Ye et al. (2023) extended the steganography method using a flow‐based model to hide more data in visualizations.…”

Section: Related Workmentioning

confidence: 99%

Interactive visual query of density maps on latent space via flow‐based models

Li,

Liang,

Jiang

et al. 2024

Transactions in GIS

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Visual querying of spatiotemporal data has become a dominant mode in the field of visual analytics. Previous studies have utilized well‐designed data structures to speed up the querying of spatiotemporal data. However, reducing storage overhead while improving the querying efficiency of data distribution remains a significant challenge. We propose a flow‐based neural representation method for efficient visual querying. First, we transform spatiotemporal data into density maps through kernel density estimation. Then, we leverage the data‐driven modeling capabilities of a flow‐based neural network to achieve a highly latent representation of the data. Various computations and queries can be performed on the latent representation to improve querying efficiency. Our experiments demonstrate that our approach achieves competitive results in visually querying spatiotemporal data in terms of storage overhead and real‐time interaction efficiency.

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