This work introduces Deep Geometric Moments (DGM) as a novel, training-free guidance mechanism for text-to-image diffusion models. Unlike existing guidance techniques (e.g., segmentation maps, depth maps, or CLIP features), which impose rigid spatial constraints or rely heavily on global semantics, DGM captures fine-grained, subject-specific visual features through robust geometric representations. The proposed method uses a pretrained DGM model during the diffusion process to steer image generation in a flexible yet identity-preserving manner. Experiments show that DGM achieves a better balance between control and diversity, enabling more nuanced and visually consistent image synthesis without retraining the diffusion model.
A privacy-preserving system that leverages Gramian Angular Field (GAF) transformations, Federated Learning, and wearable sensor data to detect Freezing of Gait (FoG) in individuals with Parkinson’s Disease
We introduces a novel Transformer-based framework, AttenGluco, designed to improve long-term blood glucose level forecasting using multimodal data—including CGM and activity signals. By integrating cross-attention and multi-scale attention mechanisms, the model effectively fuses time-series data with different sampling rates and captures long-term dependencies, outperforming a multimodal LSTM baseline by up to 12% in RMSE across multiple cohorts (healthy, prediabetes, and type 2 diabetes) using the AI-READI dataset.