In the post-deep learning era, the Transformer architecture has demonstrated its powerful
performance across pre-trained big models and various downstream tasks. However, the …

In speech separation, both CNN-and Transformer-based models have demonstrated robust
separation capabilities, garnering significant attention within the research community …

Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a
fundamental task within the realm of 3D vision. Existing learning-based filtering methods …

The advancement of natural language processing (NLP) in biology hinges on models' ability
to interpret intricate biomedical literature. Traditional models often struggle with the complex …

In the realm of time series forecasting (TSF), the Transformer has consistently demonstrated
robust performance due to its ability to focus on the global context and effectively capture …

Mamba, a special case of the State Space Model, is gaining popularity as an alternative to
template-based deep learning approaches in medical image analysis. While transformers …

Transformer, a deep neural network architecture, has long dominated the field of natural
language processing and beyond. Nevertheless, the recent introduction of Mamba …

In the Sound Event Localization and Detection (SELD) task, Transformer-based models
have demonstrated impressive capabilities. However, the quadratic complexity of the …

Venous thromboembolism (VTE) is a critical cardiovascular condition, encompassing deep
vein thrombosis (DVT) and pulmonary embolism (PE). Accurate and timely identification of …