arXiv subjects

Yuqiang Huang

Publications and source records attributed to Yuqiang Huang.

4 recordsLinked to original sources

arXiv2026

Ocean4D: Generative Underwater 4D Reconstruction via Medium-Aware Video Diffusion

Underwater 4D reconstruction remains challenging due to the coupling between degraded light transport in participating media and dynamic water variations. Most existing Methods are developed under in-air assumptions and do not explicitly account for underwater absorption and backscatter. Additionally, near-static assumptions make these approaches sensitive to drifting particles and dynamic distractors , leading to unstable geometry and inconsistent cross-view results. To address these issues, we propose a generative framework for underwater 4D reconstruction, named Ocean4D, which is built on two complementary components. Specifically, 4D-GCC constructs 4D geometrically consistent conditioning with improved cross-frame coverage, while the Medium-Aware Block performs implicit medium-aware denoising in the latent diffusion process to stabilize underwater appearance under absorption and scattering. Given a monocular video and target cameras, our method generates videos along the target trajectories while preserving global structure and cross-view consistency. Extensive experiments on both dynamic and static underwater benchmarks demonstrate state-of-the-art performance on underwater reconstruction.

cs.CV↗

arXiv2025

A Multimodal Data Fusion Attention-Empowered Generative Adversarial Network for Real Time 3D Underwater Sound Speed Field Construction

Sound speed profiles (SSPs) are crucial underwater parameters that determine the propagation patterns of acoustic signals, directly influencing the energy efficiency of underwater communication and the accuracy of positioning systems. Conventional techniques for obtaining SSPs, such as matched field processing (MFP), compressive sensing (CS), and deep learning (DL), typically depend on on-site sonar measurements, which impose stringent requirements on the deployment of underwater observation systems. To overcome this limitation and enable high-precision sound speed field reconstruction without the need for on-site underwater data collection, we propose a novel multimodal data-fusion generative adversarial network enhanced with residual attention blocks (MDF-RAGAN). This architecture integrates attention mechanisms to capture global spatial feature correlations effectively, while residual modules are employed to extract subtle perturbations in deep-ocean sound velocity distribution caused by sea surface temperature (SST) variations. Experimental results on a public real-world dataset demonstrate that the proposed model outperforms other state-of-the-art methods, achieving an estimation error of less than 0.3 m/s. Specifically, MDF-RAGAN reduces the root mean square error (RMSE) by nearly half compared to convolutional neural network (CNN) and spatial interpolation (SITP) methods, and attains a 65.8\% RMSE reduction relative to the mean profile method. These results highlight the effectiveness of multi-source fusion and cross-modal attention in enhancing the accuracy and robustness of sound speed profile reconstruction.

cs.SD↗

arXiv2025

Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Model

Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base. Here we introduce a knowledge-rich vision-language model (RetiZero) that leverages knowledge from more than 400 fundus diseases. For RetiZero's pretraining, we compiled 341,896 fundus images paired with texts, sourced from public datasets, ophthalmic literature, and online resources, encompassing a diverse range of diseases across multiple ethnicities and countries. RetiZero exhibits remarkable performance in several downstream tasks, including zero-shot disease recognition, image-to-image retrieval, AI-assisted clinical diagnosis,few-shot fine-tuning, and internal- and cross-domain disease identification. In zero-shot scenarios, RetiZero achieves Top-5 accuracies of 0.843 for 15 diseases and 0.756 for 52 diseases. For image retrieval, it achieves Top-5 scores of 0.950 and 0.886 for the same sets, respectively. AI-assisted clinical diagnosis results show that RetiZero's Top-3 zero-shot performance surpasses the average of 19 ophthalmologists from Singapore, China, and the United States. RetiZero substantially enhances clinicians' accuracy in diagnosing fundus diseases, in particularly rare ones. These findings underscore the value of integrating the RetiZero into clinical settings, where various fundus diseases are encountered.

eess.IV↗

arXiv2023

Unlocking Hidden Spins in Centrosymmetric SnSe2 by Vacancy-Controlled Spin-Orbit Scattering

Spin current generation and manipulation remain the key challenge of spintronics, in which relativistic spinorbit coupling (SOC) play a ubiquitous role. In this letter, we demonstrate that hidden Rashba spins in the non-magnetic, centrosymmetric lattice of multilayer SnSe2 can be efficiently activated by spin-orbit scattering introduced by Se vacancies. Via vacancy scattering, conduction electrons with hidden spin-momentum locked polarizations acquire out-of-plane magnetization components, which effectively break the chiral symmetry between the two Se sublattices of an SnSe2 monolayer when electron spins start precession in the strong built-in Rashba SOC field. The resulting spin separations are manifested in quantum transport as vacancy concentrationand temperature-dependent crossovers from weak antilocalization (WAL) to weak localization (WL), with the distinctive spin relaxation mechanism of the Dyakonov-Perel type. Our study shows the great potential of twodimensional systems with hidden-spin textures for spintronics.

cond-mat.mes-hall↗