Papers › Prion-ViT: Prions-Inspired Vision Transformers for Temperature prediction with Specklegrams
Prion-ViT: Prions-Inspired Vision Transformers for Temperature prediction with Specklegrams
Abhishek Sebastian, Pragna R, Sonaa Rajagopal, Muralikrishnan Mani
Fiber Specklegram Sensors (FSS) are vital for environmental monitoring due to their high temperature sensitivity, but their complex data poses challenges for predictive models. This study introduces Prion-ViT, a prion-inspired Vision Transformer model, inspired by biological prion memory mechanisms, to improve long-term dependency modeling and temperature prediction accuracy using FSS data. Prion-ViT leverages a persistent memory state to retain and propagate key features across layers, reducing mean absolute error (MAE) to 0.71°C and outperforming models like ResNet, Inception Net V2, and Standard Vision Transformers. This paper also discusses Explainable AI (XAI) techniques, providing a perspective on specklegrams through attention and saliency maps, which highlight key regions contributing to predictions
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Temperature Prediction Using Specklegrams | FSS Dataset | Prion-ViT | Average MAE | 0.52 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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