MMSD and SAMR achieve 99 percent and 99.1 percent average data reduction for traffic images by transmitting segmentation maps, edges, text or semantically masked JPEGs and reconstructing via diffusion or inpainting models.
Perceptual losses for real-time style transfer and super-resolution
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
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3DTV proposes a feedforward network for real-time sparse-view interpolation using Delaunay triplet selection and a pose-aware coarse-to-fine depth module, outperforming real-time baselines without scene-specific optimization.
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Efficient Semantic Image Communication for Traffic Monitoring at the Edge
MMSD and SAMR achieve 99 percent and 99.1 percent average data reduction for traffic images by transmitting segmentation maps, edges, text or semantically masked JPEGs and reconstructing via diffusion or inpainting models.
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3DTV: A Feedforward Interpolation Network for Real-Time View Synthesis
3DTV proposes a feedforward network for real-time sparse-view interpolation using Delaunay triplet selection and a pose-aware coarse-to-fine depth module, outperforming real-time baselines without scene-specific optimization.