State space models (SSM) with selection mechanisms and hardware-aware architectures,
namely Mamba, have recently shown significant potential in long-sequence modeling. Since …

Mamba, a recent selective structured state space model, performs excellently on long
sequence modeling tasks. Mamba mitigates the modeling constraints of convolutional …

Artificial intelligence has started to transform histopathology impacting clinical diagnostics
and biomedical research. However, while many computational pathology approaches have …

In the realm of time series forecasting (TSF), it is imperative for models to adeptly discern
and distill hidden patterns within historical time series data to forecast future states …

As one of the most representative DL techniques, Transformer architecture has empowered
numerous advanced models, especially the large language models (LLMs) that comprise …

Sequence modeling is a crucial area across various domains, including Natural Language
Processing (NLP), speech recognition, time series forecasting, music generation, and …

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 …

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 …

State Space Model (SSM) is a mathematical model used to describe and analyze the
behavior of dynamic systems. This model has witnessed numerous applications in several …

In the field of remote sensing (RS), change detection (CD) methods are critical for analyzing
the quality of images shot over various geographical areas, particularly for high-resolution …