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

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

Transformers have become one of the foundational architectures in point cloud analysis
tasks due to their excellent global modeling ability. However, the attention mechanism has …

Recent advancements in state space models, notably Mamba, have demonstrated
significant progress in modeling long sequences for tasks like language understanding. Yet …

In this work, for the first time, we demonstrate that Mamba-based point cloud methods can
outperform point-based methods. Mamba exhibits strong global modeling capabilities and …

Multimodal large language models (MLLMs) have attracted widespread interest and have
rich applications. However, the inherent attention mechanism in its Transformer structure …

The Mamba layer offers an efficient selective state space model (SSM) that is highly effective
in modeling multiple domains including NLP, long-range sequences processing, and …

Image restoration is a critical task in low-level computer vision, aiming to restore high-quality
images from degraded inputs. Various models, such as convolutional neural networks …

We tackle the challenge of efficiently reconstructing a 3D asset from a single image with
growing demands for automated 3D content creation pipelines. Previous methods primarily …

UNet and its variants have been widely used in medical image segmentation. However,
these models, especially those based on Transformer architectures, pose challenges due to …