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

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 …

Deep learning, as a vital technique, has sparked a notable revolution in artificial intelligence.
As the most representative architecture, Transformers have empowered numerous …

Time series forecasting is an important problem and plays a key role in a variety of
applications including weather forecasting, stock market, and scientific simulations. Although …

Aspect-based sentiment Analysis (ABSA) identifies and evaluates sentiments toward
specific aspects of entities within text, providing detailed insights beyond overall sentiment …

Few-shot class-incremental learning (FSCIL) confronts the challenge of integrating new
classes into a model with minimal training samples while preserving the knowledge of …

Dynamic graph learning aims to uncover evolutionary laws in real-world systems, enabling
accurate social recommendation (link prediction) or early detection of cancer cells …

Domain Generalization (DG) has been recently explored to improve the generalizability of
point cloud classification (PCC) models toward unseen domains. However, they often suffer …

Burst super-resolution aims to reconstruct high-resolution images with higher quality and
richer details by fusing the sub-pixel information from multiple burst low-resolution frames. In …

Multispectral oriented object detection faces challenges due to both inter-modal and intra-
modal discrepancies. Recent studies often rely on transformer-based models to address …