Large models based on the Transformer architecture play increasingly vital roles in artificial
intelligence, particularly within the realms of natural language processing (NLP) and …

Deep learning based on neural networks has been widely used in image recognition,
speech recognition, natural language processing, automatic driving, and other fields and …

Quantization scale and bit-width are the most important parameters when considering how
to quantize a neural network. Prior work focuses on optimizing quantization scales in a …

The transformer extends its success from the language to the vision domain. Because of the
numerous stacked self-attention and cross-attention blocks in the transformer, which involve …

Vision Transformer (ViT) architectures are becoming increasingly popular and widely
employed to tackle computer vision applications. Their main feature is the capacity to extract …

While vision transformers (ViTs) have continuously achieved new milestones in the field of
computer vision, their sophisticated network architectures with high computation and …

Vision Transformer models, such as ViT, Swin Transformer, and Transformer-in-Transformer,
have recently gained significant traction in computer vision tasks due to their ability to …

In the past year, Multimodal Large Language Models (MLLMs) have demonstrated
remarkable performance in tasks such as visual question answering, visual understanding …

The computer vision community is embracing two promising learning paradigms: the Vision
Transformer (ViT) and Multi-task Learning (MTL). ViT models show extraordinary …

While vision transformers (ViTs) have shown consistent progress in computer vision,
deploying them for real-time decision-making scenarios (< 1 ms) is challenging. Current …