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pro vyhledávání: '"Chen, Honglong"'
Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level feature maps from the frozen visual encoder to compute the pixel-wise similarity a
Externí odkaz:
http://arxiv.org/abs/2405.08458
Backdoor attacks pose serious security threats to deep neural networks (DNNs). Backdoored models make arbitrarily (targeted) incorrect predictions on inputs embedded with well-designed triggers while behaving normally on clean inputs. Many works have
Externí odkaz:
http://arxiv.org/abs/2307.10184
Federated learning (FL) aims to collaboratively train the global model in a distributed manner by sharing the model parameters from local clients to a central server, thereby potentially protecting users' private information. Nevertheless, recent stu
Externí odkaz:
http://arxiv.org/abs/2302.08044
Autor:
Li, Junjian, Chen, Honglong
Deep learning technologies have become the backbone for the development of computer vision. With further explorations, deep neural networks have been found vulnerable to well-designed adversarial attacks. Most of the vision devices are equipped with
Externí odkaz:
http://arxiv.org/abs/2206.01733
Collaborative teamwork is key to major scientific discoveries. However, the prevalence of collaboration among researchers makes team recognition increasingly challenging. Previous studies have demonstrated that people are more likely to collaborate w
Externí odkaz:
http://arxiv.org/abs/2204.02667
Publikováno v:
In Information Sciences February 2025 690
With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoTs), an increasing number of computation intensive or delay sensitive biomedical data processing and analysis tasks are produced in vehicles, bringing more and more
Externí odkaz:
http://arxiv.org/abs/2203.07999
Sparsity of user-to-item rating data becomes one of challenging issues in the recommender systems, which severely deteriorates the recommendation performance. Fortunately, context-aware recommender systems can alleviate the sparsity problem by making
Externí odkaz:
http://arxiv.org/abs/2202.10241
The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve
Externí odkaz:
http://arxiv.org/abs/2202.10236
Publikováno v:
The 20th IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (2021)
With the explosive growth of new graduates with research degrees every year, unprecedented challenges arise for early-career researchers to find a job at a suitable institution. This study aims to understand the behavior of academic job transition an
Externí odkaz:
http://arxiv.org/abs/2202.07832