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Modern data analysis and processing tasks typically involve large sets of graph structured data, where the structure carries critical information. Typically, graphs are used as mathematical tools to describe the interactions between the different entities in the system. Characterizing the meso-scale organization, i.e. the community structure, is an important task in network science. Community detection aims to partition the network into sets of nodes that are densely connected internally but sparsely connected to other dense sets of nodes.

Given its remarkable capability for feature extraction in computer vision tasks, deep learning (DL) has been extensively utilized to fuse infrared and visible images. However, the existing DL-based methods generally extract complementary information from source images through convolutional operations, which results in limited preservation of global features. To this end, we propose a novel infrared and visible image fusion method, i.e., the Y-shape dynamic Transformer (YDTR). Speciï¬

Future wireless communication systems are expected to explore spectral bands typically used by radar systems, in order to overcome spectrum congestion of traditional communication bands. Since, in many applications, radar and communication share the same platform, spectrum sharing can be facilitated by joint design as a dual-function radar-communications system.

Multimodal medical image fusion, an effective way to merge the complementary information in different modalities, has become a significant technique to facilitate clinical diagnosis and surgical navigation. However, existing deep fusion models generally depend on convolutional operations, which fails to preserve global context information. To compensate for this defect and achieve accurate fusion, we propose a multiscale adaptive Transformer to fuse multimodal medical images termed MATR.

Multiobject tracking (MOT) is a key challenge in a wide range of applications, such as autonomous navigation and applied ocean sciences. MOT is complicated by object appearance and disappearance, data association ambiguities, and occlusion. Conventional Bayesian methods for MOT, e.g., methods based on belief propagation (BP), entirely rely on a statistical model. This fully model-based approach can lead to highly suboptimal estimates when there is a mismatch between the statistical model and the true data-generating process.

Diffusion models excel at generating photorealistic images, yet their outputs betray a hidden signature: spectral artefacts — distinct anormalies in the frequency domain that deviate from natural image statistics. This webinar will unpack the mathematical underpinnings behind these artefacts, revealing why and how incorrect frequencies emerge during the generation process. We will discuss the extent to which frequency artefacts can be exploited as reliable markers to identify deepfakes, offering new tools for image forensics.

The demand for high sound quality is increasing in both entertainment and communications. Consequently, audio restoration algorithms play a critical role in mitigating distortions and interferences that originate from recording processes or arise from imperfect transmission pipelines. This webinar offers an in-depth examination of generative audio restoration algorithms, with a particular focus on diffusion-based techniques for speech enhancement.

Audio source separation is the task of extracting one or more constituent components, or composites thereof, from their mixture. Creatively produced audio signals, such as music and cinematic audio, present a unique challenge for source separation algorithms due to the sheer diversity of potential sound sources within a particular mixture. However, most state-of-the-art deep learning systems for source separation have often been either a collection of single-source separators or a tightly coupled system that cannot be easily adapted to support additional or unseen sound sources.

In this talk, the presenter will discuss the progress made over the last decade for the implementation of clinical AI solutions for digital pathology. She will draw upon both academic and industrial experiences to discuss the various stages of growth of AI for digital pathology, including the challenges of the past, where we are now, and some perspectives for the future.

Deep Neural Networks (DNNs) have had tremendous positive impact on performance in several tasks in image processing (e.g. image segmentation). In this seminar, I will present works done in my team for improving the performance of DNNs, and how efficient solutions based on DNN combined with Graphs, can be designed in applications such as automatic discovery and geotagging of objects.