Human-Aligned Generative Perception: Bridging Psychophysics and Generative Models

Text-to-image diffusion models generate highly detailed textures, yet they often rely on surface appearance and fail to follow strict geometric constraints, particularly when those constraints conflict with the style implied by the text prompt. This reflects a broader semantic gap between human perception and current generative models. We investigate whether geometric understanding can be introduced without specialized training by using lightweight, off-the-shelf discriminators as external guidance signals. We propose a Human Perception Embedding (HPE) teacher trained on the THINGS triplet dataset, which captures human sensitivity to object shape. By injecting gradients from this teacher into the latent diffusion process, we show that geometry and style can be separated in a controllable manner. We evaluate this approach across three architectures: Stable Diffusion v1.5 with a U-Net backbone, the flow-matching model SiT-XL/2, and the diffusion transformer PixArt-Σ. Our experiments reveal that flow models tend to drift back toward their default trajectories without continuous guidance, and we demonstrate zero-shot transfer of complex three-dimensional shapes, such as an Eames chair, onto conflicting materials such as pink metal. This guided generation improves semantic alignment by about 80 percent compared to unguided baselines. Overall, our results show that small teacher models can reliably guide large generative systems, enabling stronger geometric control and broadening the creative range of text-to-image synthesis.

SAMM2D: Scale-Aware Multi-Modal 2D Dual-Encoder for High-Sensitivity Intracrania Aneurysm Screening

Effective aneurysm detection is essential to avert life-threatening hemorrhages, but it remains challenging due to the subtle morphology of the aneurysm, pronounced class imbalance, and the scarcity of annotated data. We introduce SAMM2D, a dual-encoder framework that achieves an AUC of 0.686 on the RSNA intracranial aneurysm dataset; an improvement of 32% over the clinical baseline. In a comprehensive ablation across six augmentation regimes, we made a striking discovery: any form of data augmentation degraded performance when coupled with a strong pretrained backbone. Our unaugmented baseline model outperformed all augmented variants by 1.75--2.23 percentage points (p < 0.01), overturning the assumption that "more augmentation is always better" in low-data medical settings. We hypothesize that ImageNet-pretrained features already capture robust invariances, rendering additional augmentations both redundant and disruptive to the learned feature manifold. By calibrating the decision threshold, SAMM2D reaches 95% sensitivity, surpassing average radiologist performance, and translates to a projected \$13.9M in savings per 1,000 patients in screening applications. Grad-CAM visualizations confirm that 85% of true positives attend to relevant vascular regions (62% IoU with expert annotations), demonstrating the model's clinically meaningful focus. Our results suggest that future medical imaging workflows could benefit more from strong pretraining than from increasingly complex augmentation pipelines.