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

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

Vision Transformers convert images to sequences by slicing them into patches. The size of
these patches controls a speed/accuracy tradeoff, with smaller patches leading to higher …

Large pre-trained models (LPMs), such as LLaMA and GLM, have shown exceptional
performance across various tasks through fine-tuning. Although low-rank adaption (LoRA) …

Recently vision transformer models have become prominent models for a range of tasks.
These models, however, usually suffer from intensive computational costs and heavy …

To segment railway track regions in real-time for intrusion detection and improving security,
this paper proposes an efficient railway track region segmentation network (ERTNet) based …

Abstract Reconstructing a High Dynamic Range (HDR) image from several Low Dynamic
Range (LDR) images with different exposures is a challenging task, especially in the …

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

Pre-trained transformer models have achieved remarkable success in natural language
processing (NLP) and have recently become competitive alternatives to Convolution Neural …

Pruning is a widely used technique for reducing the size of deep neural networks while
maintaining their performance. However, such a technique, despite being able to massively …