TBC: A Target-Background Contrast Metric for Low-Altitude Infrared and Visible Image Fusion

Infrared and visible image fusion is a pivotal technology in low-altitude UAV reconnaissance missions, providing high-quality data support for downstream tasks such as target detection and tracking by integrating thermal saliency with background texture details.However, traditional no-reference metrics fail(Specifically,like Entropy (EN) and Average Gradient (AG)) in complex low-light environments. They often misinterpret high-frequency sensor noise as valid detail. This creates a "Noise Trap," paradoxically assigning higher scores to noisy images and misguiding fusion algorithms.To address this, we propose the Target-Background Contrast (TBC) metric. Inspired by Weber's Law, TBC focuses on the relative contrast of salient targets rather than global statistics. Unlike traditional metrics, TBC penalizes background noise and rewards target visibility. Experiments on the DroneVehicle dataset demonstrate that TBC aligns better with human perception and provides a reliable standard for low-altitude scenarios.

AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks

Accurate drug response prediction (DRP) is a crucial yet challenging task in precision medicine. This paper presents a novel Attention-Guided Multi-omics Integration (AGMI) approach for DRP, which first constructs a Multi-edge Graph (MeG) for each cell line, and then aggregates multi-omics features to predict drug response using a novel structure, called Graph edge-aware Network (GeNet). For the first time, our AGMI approach explores gene constraint based multi-omics integration for DRP with the whole-genome using GNNs. Empirical experiments on the CCLE and GDSC datasets show that our AGMI largely outperforms state-of-the-art DRP methods by 8.3%--34.2% on four metrics. Our data and code are available at https://github.com/yivan-WYYGDSG/AGMI.