Singing melody extraction from polyphonic music is a complex but important task in music information retrieval. Most existing models struggle with adaptive multi-scale feature extraction and long-range dependency modeling, often resulting in common artifacts such as octave errors and pitch fragmentation. To address this, we propose the Cross-Scale Transformer Network (CSTNet), a novel deep learning architecture that dynamically captures and fuses multi-scale temporal and spectral features. The CSTNet introduces two key components: the Multi scale Time-Frequency Aggregation (MF-TFA) module for adaptive local feature representation across resolutions, and Feature Cross Transformer Block (FCTB), which enables explicit cross scale interaction through a global channel-wise cross-attention mechanism. Experimental results show that our proposed method outperforms five compared state-of-the-art methods, achieving overall accuracy scores of 87.3%, 90.5%, and 77.4% on the ADC 2004, MIREX 05, and MEDLEY DB datasets, respectively. Visualization results demonstrate that CSTNet effectively reduces octave errors.

This paper investigates a low-light image enhancement method based on the diffusion bridge framework. Currently, low-light image enhancement tasks still face challenges in noise reduction and detail restoration, and existing diffusion model methods are time-consuming and have unstable diffusion processes. We conducted an in-depth study of diffusion bridge theory, integrating the advantages of the end-to-end paradigm of diffusion bridge theory and employing a nonlinear activation network to further enhance the performance of low-light image enhancement tasks. Additionally, we used a Gamma correction module for fine-tuning low-light images, significantly improving performance with almost no extra computational cost. Experiments show that Low-Light image enhancement with Difffusion Bridge (LLDB) far surpasses other methods on LOLv1 and LOLv2 datasets. Code is available at https://github.com/M-Chase/LLDB

Image restoration refers to the process of restoring a damaged low-quality image back to its corresponding high-quality image. Recently, a special type of diffusion bridge model has achieved more advanced results in image restoration. It can transform the direct mapping from low-quality to high-quality images into a diffusion process, restoring low-quality images through a reverse process. However, the current diffusion bridge restoration models do not emphasize the idea of conditional control, which may affect performance. This paper introduces the ECDB model enhancing the control of the diffusion bridge with low-quality images as conditions. Moreover, in response to the characteristic of diffusion models having low denoising level at larger values of t, we also propose a Conditional Fusion Schedule, which more effectively handles the conditional feature information of various modules. Experimental results prove that the ECDB model has achieved state-of-the-art results in many image restoration tasks, including deraining, inpainting and super-resolution. Code is avaliable at https://github.com/Hammour-steak/ECDB.

Text-based image editing has advanced significantly with large-scale text-to-image models, but challenges remain: (1) inversion-based methods require substantial resources for inversion and optimization; (2) inversion-free methods struggle to balance controllability and consistency; and (3) incompatibility with faster flow-based models and Diffusion Transformers. In this paper, we propose FlowEdit, an inversion-free text-based image editing framework based on Diffusion Transformers. FlowEdit uses flow consistency sampling for precise image reconstruction, and enhances target images with robust consistency through velocity rectify and attention control mechanism. Extensive experiments demonstrate that FlowEdit excels in editing capabilities and consistency across various tasks while maintaining high efficiency (under 4 seconds on a single 3090 GPU), making it suitable for real-time applications. Our codes are available at https://github.com/XavierPeng319/FlowEdit.git

Diffusion models exhibit powerful generative capabilities enabling noise mapping to data via reverse stochastic differential equations. However, in image restoration, the focus is on the mapping relationship from low-quality to high-quality images. Regarding this issue, we introduce the Generalized Ornstein-Uhlenbeck Bridge (GOUB) model. By leveraging the natural mean-reverting property of the generalized OU process and further eliminating the variance of its steady-state distribution through the Doob's *h*–transform, we achieve diffusion mappings from point to point enabling the recovery of high-quality images from low-quality ones. Moreover, we unravel the fundamental mathematical essence shared by various bridge models, all of which are special instances of GOUB and empirically demonstrate the optimality of our proposed models. Additionally, we present the corresponding Mean-ODE model adept at capturing both pixel-level details and structural perceptions. Experimental outcomes showcase the state-of-the-art performance achieved by both models across diverse tasks, including inpainting, deraining, and super-resolution. Code is available at https://github.com/Hammour-steak/GOUB.

Identifying drug target candidates is an important task for early development throughout the drug discovery process. This process is supported by the development of new high-throughput technologies that enable better understanding of disease mechanism. With the push for personalized medicine, more experimental data are produced to identify how the genetics differ among individuals with respect to disease mechanism and drug response. It becomes critical to facilitate effective analysis of the large amount of biological data. In this paper, we describe our solution in employing text mining as a technique for finding scientific information for target and biomarker discovery from the biomedical literature. Additionally, we discuss how the extracted knowledge can be an effective resource for the analysis of biological data such as next-generation sequencing data.