Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

The remarkable success of deep learning in various domains relies on the availability of
large-scale annotated datasets. However, obtaining annotations is expensive and requires …

Scale is the primary factor for building a powerful foundation model that could well
generalize to a variety of downstream tasks. However, it is still challenging to train video …

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 …

Driven by improved architectures and better representation learning frameworks, the field of
visual recognition has enjoyed rapid modernization and performance boost in the early …

We present an approach to reconstruct humans and track them over time. At the core of our
approach, we propose a fully" transformerized" version of a network for human mesh …

Abstract Segment Anything Model (SAM) has emerged as a powerful tool for numerous
vision applications. A key component that drives the impressive performance for zero-shot …

Most state-of-the-art approaches for weather and climate modeling are based on physics-
informed numerical models of the atmosphere. These approaches aim to model the non …

Modern hierarchical vision transformers have added several vision-specific components in
the pursuit of supervised classification performance. While these components lead to …

With the success of large language models (LLMs) integrating the vision model into LLMs to
build vision-language foundation models has gained much more interest recently. However …