Editors Pick

Researchers from The University of Sydney have introduced EfficientVMamba, a new model that optimizes efficiency in computer vision tasks. This groundbreaking architecture effectively blends the strengths of Convolutional Neural Networks (CNNs) and Transformer-based models, known for their prowess in local feature extraction and global information processing respectively. The EfficientVMamba approach incorporates an atrous-based selective scanning…

High-resolution image synthesis has always been a challenge in digital imagery due to issues such as the emergence of repetitive patterns and structural distortions. While pre-trained diffusion models have been effective, they often result in artifacts when it comes to high-resolution image generation. Despite various attempts, such as enhancing the convolutional layers of these models,…

In the field of computer science, accurately reconstructing 3D models from 2D images—a problem known as pose inference—presents complex challenges. For instance, the task can be vital in producing 3D models for e-commerce or assisting in autonomous vehicle navigation. Existing methods rely on gathering the camera poses prior, or harnessing generative adversarial networks (GANs), but…

Video understanding, which involves parsing and interpreting visual content and temporal dynamics within video sequences, is a complex domain. Traditional methods like 3D convolutional neural networks (CNNs) and video transformers have seen steady advancement, but often they fail to effectively manage local redundancy and global dependencies. Amidst this, the emergence of the VideoMamba, developed based…