Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Shen, Jiaxing"'
Publikováno v:
International Journal of Computer Vision (2024)
With the rapid development of depth sensor, more and more RGB-D videos could be obtained. Identifying the foreground in RGB-D videos is a fundamental and important task. However, the existing salient object detection (SOD) works only focus on either
Externí odkaz:
http://arxiv.org/abs/2406.12536
Wearable data serves various health monitoring purposes, such as determining activity states based on user behavior and providing tailored exercise recommendations. However, the individual data perception and computational capabilities of wearable de
Externí odkaz:
http://arxiv.org/abs/2405.10979
Generating appropriate emotions for responses is essential for dialog systems to provide human-like interaction in various application scenarios. Most previous dialog systems tried to achieve this goal by learning empathetic manners from anonymous co
Externí odkaz:
http://arxiv.org/abs/2404.07229
Manual network configuration automation (NCA) tools face significant challenges in versatility and flexibility due to their reliance on extensive domain expertise and manual design, limiting their adaptability to diverse scenarios and complex applica
Externí odkaz:
http://arxiv.org/abs/2403.09369
Pre-trained language models (PLMs) leverage chains-of-thought (CoT) to simulate human reasoning and inference processes, achieving proficient performance in multi-hop QA. However, a gap persists between PLMs' reasoning abilities and those of humans w
Externí odkaz:
http://arxiv.org/abs/2402.19350
Autor:
Chen, Kongyang, Wang, Zixin, Mi, Bing, Liu, Waixi, Wang, Shaowei, Ren, Xiaojun, Shen, Jiaxing
Recently, large language models (LLMs) have emerged as a notable field, attracting significant attention for its ability to automatically generate intelligent contents for various application domains. However, LLMs still suffer from significant secur
Externí odkaz:
http://arxiv.org/abs/2404.16841
The rapid evolution of Internet of Things (IoT) technology has spurred the widespread adoption of Human Activity Recognition (HAR) in various daily life domains. Federated Learning (FL) is frequently utilized to build a global HAR model by aggregatin
Externí odkaz:
http://arxiv.org/abs/2404.03659
Personalized federated learning (PFL) is a popular framework that allows clients to have different models to address application scenarios where clients' data are in different domains. The typical model of a client in PFL features a global encoder tr
Externí odkaz:
http://arxiv.org/abs/2307.13995
Federated learning (FL) is a new paradigm for distributed machine learning that allows a global model to be trained across multiple clients without compromising their privacy. Although FL has demonstrated remarkable success in various scenarios, rece
Externí odkaz:
http://arxiv.org/abs/2306.02701
Publikováno v:
In Engineering Applications of Artificial Intelligence July 2024 133 Part F