Academic Background With the rapid growth of digital content, the importance of digital signatures in identity verification and content authentication has become increasingly prominent. However, the security and integrity of digital signatures face significant challenges. To protect the authenticity of signatures and prevent tampering, digital wate...
Academic Background In the era of big data, distributed learning has become an effective method for solving large-scale online machine learning problems. However, frequent communication and projection operations in distributed learning incur high communication and computational costs. Especially in high-dimensional constrained optimization problems...
Academic Background In the field of computer vision, rain streaks are a common interference factor, especially in outdoor surveillance, autonomous driving, and intelligent transportation systems. Rain streaks significantly degrade image quality, affecting the recognition and analysis capabilities of visual systems. Traditional rain streak removal m...
Academic Background With the rapid development of artificial intelligence (AI) and natural language processing (NLP), large language models (LLMs) have made significant progress in both academia and industry. However, despite the outstanding performance of LLMs in multiple NLP tasks, no single model has been able to simultaneously meet all task req...
Background Introduction In the field of computer vision, research on visual attention models aims to simulate how the human visual system selects regions of interest from images or natural scenes. The human brain’s ability to quickly and accurately identify salient regions in visual scenes is of great significance in tasks such as image processing,...
Academic Background In recent years, Chinese word embeddings have attracted significant attention in the field of Natural Language Processing (NLP). Unlike English, the complex and diverse structure of Chinese characters presents unique challenges for semantic representation. Traditional word vector models, such as Word2Vec, often fail to fully cap...