Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

Transformer has shown excellent performance in remote sensing field with long-range
modeling capabilities. Remote sensing video (RSV) moving object detection and tracking …

In the past decade, advances in deep learning have resulted in breakthroughs in a variety of
areas, including computer vision, natural language understanding, speech recognition, and …

We investigate the potential of GPT-4~\cite {gpt4} to perform Neural Architecture Search
(NAS)--the task of designing effective neural architectures. Our proposed approach,\textbf …

Abstract Human-Object Interaction (HOI) detection is a challenging computer vision task that
requires visual models to address the complex interactive relationship between humans and …

This paper explores the feasibility of finding an optimal sub-model from a vision transformer
and introduces a pure vision transformer slimming (ViT-Slim) framework. It can search a sub …

Transformer-based Deep Neural Network architectures have gained tremendous interest
due to their effectiveness in various applications across Natural Language Processing (NLP) …

Designing accurate and efficient vision transformers (ViTs) is an important but challenging
task. Supernet-based one-shot neural architecture search (NAS) enables fast architecture …

Recent advancements in state space models, notably Mamba, have demonstrated
significant progress in modeling long sequences for tasks like language understanding. Yet …