In computer vision, a series of exemplary advances have been made in several areas
involving image classification, semantic segmentation, object detection, and image super …

Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …

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 …

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

Abstract Convolutional Neural Networks (CNNs) have achieved remarkable performance in
various computer vision tasks but this comes at the cost of tremendous computational …

Abstract Recently lightweight Vision Transformers (ViTs) demonstrate superior performance
and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on …

The quadratic computation complexity of self-attention has been a persistent challenge
when applying Transformer models to vision tasks. Linear attention, on the other hand, offers …

We present the latest generation of MobileNets: MobileNetV4 (MNv4). They feature
universally-efficient architecture designs for mobile devices. We introduce the Universal …

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 …

High-resolution dense prediction enables many appealing real-world applications, such as
computational photography, autonomous driving, etc. However, the vast computational cost …