Yucheng Xing

Yucheng Xing, Xin Wang

Rain streaks manifest as directional and frequency-concentrated structures that overlap across multiple scales, making single-image rain removal particularly challenging. While diffusion-based restoration models provide a powerful framework for progressive denoising, standard spatial-domain diffusion does not explicitly account for such structured spectral characteristics. We introduce SpectralDiff, a spectral-structured diffusion-based framework tailored for single-image rain removal. Rather than redefining the diffusion formulation, our method incorporates structured spectral perturbations to guide the progressive suppression of multi-directional rain components. To support this design, we further propose a full-product U-Net architecture that leverages the convolution theorem to replace convolution operations with element-wise product layers, improving computational efficiency while preserving modeling capacity. Extensive experiments on synthetic and real-world benchmarks demonstrate that SpectralDiff achieves competitive rain removal performance with improved model compactness and favorable inference efficiency compared to existing diffusion-based approaches.

Ling Huang, Yucheng Xing, Qika Lin et al.

Multimodal survival analysis aims to combine heterogeneous data sources (e.g., clinical, imaging, text, genomics) to improve the prediction quality of survival outcomes. However, this task is particularly challenging due to high heterogeneity and noise across data sources, which vary in structure, distribution, and context. Additionally, the ground truth is often censored (uncertain) due to incomplete follow-up data. In this paper, we propose a novel evidential multimodal survival fusion model, EsurvFusion, designed to combine multimodal data at the decision level through an evidence-based decision fusion layer that jointly addresses both data and model uncertainty while incorporating modality-level reliability. Specifically, EsurvFusion first models unimodal data with newly introduced Gaussian random fuzzy numbers, producing unimodal survival predictions along with corresponding aleatoric and epistemic uncertainties. It then estimates modality-level reliability through a reliability discounting layer to correct the misleading impact of noisy data modalities. Finally, a multimodal evidence-based fusion layer is introduced to combine the discounted predictions to form a unified, interpretable multimodal survival analysis model, revealing each modality's influence based on the learned reliability coefficients. This is the first work that studies multimodal survival analysis with both uncertainty and reliability. Extensive experiments on four multimodal survival datasets demonstrate the effectiveness of our model in handling high heterogeneity data, establishing new state-of-the-art on several benchmarks.

Yucheng Xing, Xiaodong Liu, Xin Wang

With the development of artificial intelligence, more and more attention has been put onto generative models, which represent the creativity, a very important aspect of intelligence. In recent years, diffusion models have been studied and proven to be more reasonable and effective than previous methods. However, common diffusion frameworks suffer from controllability problems. Although extra conditions have been considered by some work to guide the diffusion process for a specific target generation, it only controls the generation result but not its process. In this work, we propose a new adaptive framework, $\textit{Adaptively Controllable Diffusion (AC-Diff) Model}$, to automatically and fully control the generation process, including not only the type of generation result but also the length and parameters of the generation process. Both inputs and conditions will be first fed into a $\textit{Conditional Time-Step (CTS) Module}$ to determine the number of steps needed for a generation. Then according to the length of the process, the diffusion rate parameters will be estimated through our $\textit{Adaptive Hybrid Noise Schedule (AHNS) Module}$. We further train the network with the corresponding adaptive sampling mechanism to learn how to adjust itself according to the conditions for the overall performance improvement. To enable its practical applications, AC-Diff is expected to largely reduce the average number of generation steps and execution time while maintaining the same performance as done in the literature diffusion models.