To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

Transformer is a new kind of neural architecture which encodes the input data as powerful
features via the attention mechanism. Basically, the visual transformers first divide the input …

Abstract Knowledge distillation is a widely used paradigm for inheriting information from a
complicated teacher network to a compact student network and maintaining the strong …

The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Filter pruning has been widely used for neural network compression because of its enabled
practical acceleration. To date, most of the existing filter pruning works explore the …

This paper studies the efficiency problem for visual transformers by excavating redundant
calculation in given networks. The recent transformer architecture has demonstrated its …

Event-based visual, a new visual paradigm with bio-inspired dynamic perception and μ s
level temporal resolution, has prominent advantages in many specific visual scenarios and …

Recent years have witnessed great progress in deep neural networks for real-time
applications. However, most existing works do not explicitly consider the general case …

Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …