Vision systems to see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

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

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Foundation models, now powering most of the exciting applications in deep learning, are
almost universally based on the Transformer architecture and its core attention module …

Recently the state space models (SSMs) with efficient hardware-aware designs, ie, the
Mamba deep learning model, have shown great potential for long sequence modeling …

Abstract Vision Transformer (ViT) has gained increasing attention in the computer vision
community in recent years. However the core component of ViT Self-Attention lacks explicit …

A major limitation for the broader scope of problems solvable by transformers is the
quadratic scaling of computational complexity with input size. In this study, we investigate …

Addressing the dual challenges of local redundancy and global dependencies in video
understanding, this work innovatively adapts the Mamba to the video domain. The proposed …

Transformers with linear attention allow for efficient parallel training but can simultaneously
be formulated as an RNN with 2D (matrix-valued) hidden states, thus enjoying linear (with …

Emerging as fundamental building blocks for diverse artificial intelligence applications,
foundation models have achieved notable success across natural language processing and …