DART is a cross-modal foundation model that delivers rope damage classification, severity regression, and few-shot recognition from a single frozen representation trained on 4270 images across 14 damage classes.
Model-agnostic meta-learning for fast adaptation of deep networks
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
Transfer learning with pretraining-fine-tuning improves neural parameter estimation accuracy for building RC models by 18.6-49.4% over baselines while removing the need for initial parameter guesses.
citing papers explorer
-
DART: A Vision-Language Foundation Model for Comprehensive Rope Condition Monitoring
DART is a cross-modal foundation model that delivers rope damage classification, severity regression, and few-shot recognition from a single frozen representation trained on 4270 images across 14 damage classes.
-
Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
-
Transfer Learning for Neural Parameter Estimation applied to Building RC Models
Transfer learning with pretraining-fine-tuning improves neural parameter estimation accuracy for building RC models by 18.6-49.4% over baselines while removing the need for initial parameter guesses.