Latest Updates

Considerable efforts have been dedicated to exploring methods that enhance the expressiveness of graph neural networks. Current endeavors primarily focus on modifying...

 This study explores the application of Retrieval-Augmented Generation (RAG) for question answering in radiology, an area where intelligent systems can significantly...

Academic writing poses significant challenges for students, particularly non-native English speakers. While existing tools, such as Grammarly, provide surface-level...

Most graph neural networks (GNNs) utilize approximations of the general graph convolution derived in the graph Fourier domain. While GNNs are typically applied in the...

Zero-shot classifiers based on Contrastive Language-Audio Pretraining (CLAP) models enable classification of given audio into classes defined at test time using text....

Existing autonomous driving datasets are predominantly oriented towards well-structured urban settings and favourable weather conditions, leaving the complexities of...

To operate safely, autonomous vehicles (AVs) need to detect and handle unexpected objects or anomalies on the road. While significant research exists for anomaly...

Autonomous driving has the potential to significantly enhance productivity and provide numerous societal benefits. Ensuring robustness in these safety-critical systems...

Sa2VA is a recent model for language-guided dense grounding in images and video that achieves state-of-the-art results on multiple segmentation benchmarks and that has...

The detection and classification of exfoliated two-dimensional (2D) material flakes from optical microscope images can be automated using computer vision algorithms....