no code implementations • 14 May 2024 • Jin Wang, Bingfeng Zhang, Jian Pang, Honglong Chen, Weifeng Liu
Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes.
no code implementations • 3 Jul 2023 • Yudong Gao, Honglong Chen, Peng Sun, Junjian Li, Anqing Zhang, Zhibo Wang
Then, to attain strong stealthiness, we incorporate Fourier Transform and Discrete Cosine Transform to mix the poisoned image and clean image in the frequency domain.
no code implementations • 16 Feb 2023 • Zhe Li, Honglong Chen, Zhichen Ni, Huajie Shao
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.
no code implementations • 2 Jun 2022 • Junjian Li, Honglong Chen
Most of the vision devices are equipped with image signal processing (ISP) pipeline to implement RAW-to-RGB transformations and embedded into data preprocessing module for efficient image processing.
no code implementations • 16 Feb 2022 • Xiangtai Chen, Tao Tang, Jing Ren, Ivan Lee, Honglong Chen, Feng Xia
We devise an unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax) which aggregates attention mechanism and mutual information for institution recommendation.
no code implementations • 16 Feb 2022 • Zhu Wang, Honglong Chen, Zhe Li, Kai Lin, Nan Jiang, Feng Xia
Fortunately, context-aware recommender systems can alleviate the sparsity problem by making use of some auxiliary information, such as the information of both the users and items.
no code implementations • 16 Feb 2022 • Honglong Chen, Zhe Li, Zhu Wang, Zhichen Ni, Junjian Li, Ge Xu, Abdul Aziz, Feng Xia
As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data.
no code implementations • 16 Feb 2022 • Zhichen Ni, Honglong Chen, Zhe Li, Xiaomeng Wang, Na Yan, Weifeng Liu, Feng Xia
The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge.