Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

The recent wave of AI-generated content (AIGC) has witnessed substantial success in
computer vision, with the diffusion model playing a crucial role in this achievement. Due to …

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 …

The scaling of Transformers has driven breakthrough capabilities for language models. At
present, the largest large language models (LLMs) contain upwards of 100B parameters …

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 …

When enhancing low-light images, many deep learning algorithms are based on the Retinex
theory. However, the Retinex model does not consider the corruptions hidden in the dark or …

The pursuit of controllability as a higher standard of visual content creation has yielded
remarkable progress in customizable image synthesis. However, achieving controllable …

This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a
text-to-image synthesis model (ie, Stable Diffusion). ModelScopeT2V incorporates spatio …

Recently integrating video foundation models and large language models to build a video
understanding system can overcome the limitations of specific pre-defined vision tasks. Yet …

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