Predicting the future trajectories of traffic agents is a pivotal aspect in achieving collision-free
driving for autonomous vehicles. Although the overall accuracy of existing prediction …

Tabular datasets play a crucial role in various applications. Thus, developing efficient,
effective, and widely compatible prediction algorithms for tabular data is important. Currently …

Dense prediction is a fundamental requirement for many medical vision tasks such as
medical image restoration, registration, and segmentation. The most popular vision model …

Popular transformer-based detectors detect objects in a one-to-one manner, where both the
bounding box and category of each object are predicted only by the single query, leading to …

Infrared small target detection faces various challenges such as long distances, weak
features, and small scales. While methodologies based on convolutional neural networks …

Convolutional neural networks are widely used in various segmentation tasks in medical
images. However, they are challenged to learn global features adaptively due to the …

Modeling in Computer Vision has evolved to MLPs. Vision MLPs naturally lack local
modeling capability, to which the simplest treatment is combined with convolutional layers …

The accurate extraction of pertinent information from fundus images is of paramount
importance for the diagnosis of glaucoma. For a considerable period, researchers engaged …

At present, deep learning has limited application in the field of financial credit risk because
deep learning is good at processing unstructured data such as images, voice, and text, while …