SSDA uses spectral magnitude alignment and structural-guided low-rank adaptation to close frequency and adjacency gaps when large vision models process time series rendered as images.
Foundation models for time series analysis: A tutorial and survey
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
JAM aligns frozen vision and language models via joint autoencoders and multimodal Spread Loss, reliably inducing cross-modal alignment across layer depths, objectives, and model scales.
citing papers explorer
-
SSDA: Bridging Spectral and Structural Gaps via Dual Adaptation for Vision-Based Time Series Forecasting
SSDA uses spectral magnitude alignment and structural-guided low-rank adaptation to close frequency and adjacency gaps when large vision models process time series rendered as images.