24 accepted papers
Knowledge Distillation (KD) promises to bridge the gap between the high computational costs of Transformer-based models and the expressiveness limitations of linear models in long-term time series forecasting. Existing time series distillation methods inherit the computer vision paradigm, constraini…
Multi-Agent Reinforcement Learning (MARL) has been widely applied to automated aircraft conflict resolution due to its strong capability for cooperative control and distributed decision-making. However, existing approaches typically assume a fixed number of aircraft and neglect the unique characteri…
Catastrophic forgetting poses a significant challenge for graph neural networks in continuously updating their knowledge base with data streams. To address this issue, much of the research has focused on node-level continual learning using parameter regularization or rehearsal-based strategies, whil…
Heterogeneous graphs, which are common in real-world downstream tasks, have recently sparked a wave of research interest. The performance of end-to-end heterogeneous graph neural networks (HGNNs) greatly relies on supervised training for specific tasks. To reduce the labeling cost, the "pretrain-fin…
Brain cognitive state recognition based on functional Magnetic Resonance Imaging(fMRI) can capture brain functional activities under different tasks and help understand the neural mechanisms of the brain, which has always been one of the focuses of neuroscience research. Different from the predictio…
Analyzing gigapixel Whole Slide Images (WSIs) is challenging due to the complex pathological tissue environment and the absence of target-driven domain knowledge. Previous methods incorporated pathological priors to mitigate this issue but relied on additional inference steps and specialized workflo…
Remote sensing image change detection (CD) seeks to analyze and discern changes in surface objects through the use of multi-temporal remote sensing imagery. However, as image resolution advances, existing methods often fall short in capturing comprehensive visual feature representations, and their n…
Graph data augmentation proves to be an effective approach for enhancing the performance of graph classification. However, due to the complex structure of graphs, the semantic meanings of graphs are sensitive to minor modifications, while the labels of augmented graphs remain identical, thus limitin…
Graph Neural Network (GNN) is powerful in solving various graph-related tasks, while its message passing mechanism may lead to latency during inference time. Multi-Layer-Perceptron (MLP) can achieve fast inference speed but with limited performance. One solution to fill this gap is through Knowledge
Graph Neural Networks (GNNs) are widely used for node classification tasks but often fail to generalize when training and test nodes come from different distributions, limiting their practicality. To address this challenge, recent approaches have adopted invariant learning and sample reweighting tec…
We present a novel deep learning-based framework: Embedded Feature Similarity Optimization with Specific Parameter Initialization (SOPI) for 2D/3D medical image registration which is a most challenging problem due to the difficulty such as dimensional mismatch, heavy computation load and lack of gol…
Survival prediction is a significant challenge in cancer management. Tumor micro-environment is a highly sophisticated ecosystem consisting of cancer cells, immune cells, endothelial cells, fibroblasts, nerves and extracellular matrix. The intratumor heterogeneity and the interaction across multiple…
Deep learning and Convolutional Neural Networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency in-formation present obstacles in certain tasks like interpreting global structures or managing smooth transition images. Despite t…
"We present a novel image editing scenario termed Text-grounded Object Generation (TOG), defined as generating a new object in the real image spatially conditioned by textual descriptions. Existing diffusion models exhibit limitations of spatial perception in complex real-world scenes, relying on ad…
Learning about the cognitive state of the brain has always been a popular topic. Based on the fact that fluctuations of brain signals and functional connectome (FC) relate to specific human behaviors, deep learning based methods have shown promising results on the prediction of such behaviors by ana…
Graph contrastive learning has gained popularity due to its success in self-supervised graph representation learning. Augmented views in contrastive learning greatly determine the quality of the learned representations. Handcrafted data augmentations in previous work require tedious trial-and- error…
Multimodal fusion has become an important research technique in neuroscience that completes downstream tasks by extracting complementary information from multiple modalities. Existing multimodal research on brain networks mainly focuses on two modalities, structural connectivity (SC) and functional…
Exploring the mapping between structural connectivity (SC) and functional connectivity (FC) is of essential importance to understanding the working mechanism of the human brain. Traditional methods are difficult to represent the complex relationship of high-order interaction between SC and FC. Recen…
Most existing graph neural networks work under a class-balanced assumption, while ignoring class-imbalanced scenarios that widely exist in real-world graphs. Although there are many methods in other fields that can alleviate this issue, they do not consider the special topology of the non-Euclidean…
Accurate semantic segmentation of medical images is of significant importance for subsequent processing and analysis. The encoder-decoder deep learning framework has been widely applied for numerous medical image segmentation tasks. However, most existing approaches are restricted by the limited rec…
Graph neural network (GNN) is popular now to solve the tasks in non-Euclidean space and most of them learn deep embeddings by aggregating the neighboring nodes. However, these methods are prone to some problems such as over-smoothing because of the single-scale perspective field and the nature of lo…
Numerous evidence has demonstrated the pathophysiology of a number of mental disorders is intimately associated with abnormal changes of dysfunctional integration of brain network. Functional connectome (FC) exhibits a strong discriminative power for mental disorder identification. However, existing…
Prediction of brain functional activity is of great significance for neuroscience research. The brain functional activities at different regions are highly related, and their relationships can be captured with functional connectivity and structural connectivity. The existing works are challenging to…
Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation. However, further adoptio…