TL;DR
Astro-DBA, an Astronomical Dual-View DenseNet-BiLSTM-Attention architecture, successfully localizes transit regions in noisy observations and focuses on physically meaningful ingress, trough, and egress structures, supporting transparent and reliable exoplanet candidate classification.
The PI-dualformer is proposed, a novel hybrid architecture that synergizes the local feature extraction of U-Nets, the global context modeling of Linear Transformers (Linformers), and the spectral precision of Dual-Domain (FFT) learning that offers a scientifically robust and computationally efficient solution for the...
We present LUMA, a convolutional neural network (CNN) pipeline for the automated detection of strong gravitational lenses in simulated astronomical imaging. The method combines a physically motivated preprocessing stage, which enhances faint arc and ring features, with a compact three-block CNN trained using class rewe...
Feed-forward 3D reconstruction models have achieved impressive performance by scaling model and dataset size, but their cost excludes most research groups and precludes edge deployment. Additionally, generating 3D supervision without sensors still relies on slow, unreliable Structure-from-Motion, as the community lacks...
Recent advances in single image super-resolution (SISR) have leveraged convolutional neural networks (CNNs) and vision transformers to model pixel-level statistics, often relying on increasingly complex architectures to capture spatial correlations. However, these approaches generally overlook a fundamental distinction...
Visible–Thermal (VT) object detection provides stable and all-weather perception for autonomous systems and remote-sensing intelligence. However, Tiny-Object Detection (TOD) remains a persistent bottleneck due to severe feature scarcity and distinct cross-modality distribution gaps. At tiny scales, targets offer near-z...
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