The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Vision transformers have shown great success due to their high model capabilities.
However, their remarkable performance is accompanied by heavy computation costs, which …

To design fast neural networks, many works have been focusing on reducing the number of
floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does …

Designing a high-efficiency and high-quality expressive network architecture has always
been the most important research topic in the field of deep learning. Most of today's network …

With the success of Vision Transformers (ViTs) in computer vision tasks, recent arts try to
optimize the performance and complexity of ViTs to enable efficient deployment on mobile …

Self-supervised monocular depth estimation that does not require ground truth for training
has attracted attention in recent years. It is of high interest to design lightweight but effective …

This paper presents a new vision Transformer, Scale Aware Modulation Transformer (SMT),
that can handle various downstream tasks efficiently by combining the convolutional network …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

Self-attention has become a defacto choice for capturing global context in various vision
applications. However, its quadratic computational complexity with respect to image …