14 accepted papers
Single-domain generalization is essential for object detection, particularly when training models on a single source domain and evaluating them on unseen target domains. Domain shifts, such as changes in weather, lighting, or scene conditions, pose significant challenges to the generalization abilit…
Despite the promising results of disentangled representation learning in discovering latent patterns in graph-structured data, few studies have explored disentanglement for hypergraph-structured data. Integrating hyperedge disentanglement into hypergraph neural networks enables models to leverage hi…
Spatially Resolved Transcriptomics (SRT) is a cutting-edge technique that captures the spatial context of cells within tissues, enabling the study of complex biological networks. Recent graph-based methods leverage both gene expression and spatial information to identify relevant spatial domains. Ho…
Reliable navigation of autonomous vessels critically depends on robust situational awareness, particularly object detection. For this, an accurate, 360-degree perception of the surrounding environment is essential. However, most existing datasets lack the comprehensive multi-view data required for t
This study addresses the challenge of robust object detection in maritime environments, where dynamic conditions such as fog, brightness variations, and motion blur can degrade accuracy. We propose a novel framework, Joint Semantic Learning (JSL), which combines ocean scene segmentation and object d
Federated Learning (FL) on graphs enables collaborative model training to enhance performance without compromising the privacy of each client. However, existing methods often overlook the mutable nature of graph data, which frequently introduces new nodes and leads to shifts in label distribution. S…
"Multi-agent trajectory prediction is crucial to autonomous driving and understanding the surrounding environment. Learning-based approaches for multi-agent trajectory prediction, such as primarily relying on graph neural networks, graph transformers, and hypergraph neural networks, have demonstrate…
Molecular relational learning, whose goal is to learn the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications. Recently, graph neural networks have recently shown great success in molecular relational learning by modeling…
Routine clinical visits of a patient produce not only image data, but also non-image data containing clinical information regarding the patient, i.e., medical data is multi-modal in nature. Such heterogeneous modalities offer different and complementary perspectives on the same patient, resulting in…
Inspired by the recent success of self-supervised methods applied on images, self-supervised learning on graph structured data has seen rapid growth especially centered on augmentation-based contrastive methods. However, we argue that without carefully designed augmentation techniques, augmentations…
"Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images. We propose an attention similarity knowledge distillation approach, which transfers attention maps obtained from a high resolution (HR) network a…
Interests in exploration of new energy resources are increasing due to the exhaustion of existing resources. To explore new energy sources, various studies have been conducted to improve the drilling performance of drilling equipment for deep and strong ground. However, with better performance, the
We present RAPPIDS: a novel collision checking and planning algorithm for multicopters that is capable of quickly finding local collision-free trajectories given a single depth image from an onboard camera. The primary contribution of this work is a new pyramid-based spatial partitioning method that
Recently in tandem with the spread of portable devices for reading electronic books, devices for digitizing paper books, called book scanners, are developed to meet the increased demand for digitizing privately owned books. However, conventional book scanners still have complex components to mechani…