Search Results for author: Pengfei Hu

Found 15 papers, 5 papers with code

Incentivizing Massive Unknown Workers for Budget-Limited Crowdsensing: From Off-Line and On-Line Perspectives

no code implementations21 Sep 2023 Feng Li, Yuqi Chai, Huan Yang, Pengfei Hu, Lingjie Duan

Moreover, the standard CMAB usually assumes the workers always stay in the system, whereas the workers may join in or depart from the system over time, such that what we have learnt for an individual worker cannot be applied after the worker leaves.

Speech2Lip: High-fidelity Speech to Lip Generation by Learning from a Short Video

no code implementations ICCV 2023 Xiuzhe Wu, Pengfei Hu, Yang Wu, Xiaoyang Lyu, Yan-Pei Cao, Ying Shan, Wenming Yang, Zhongqian Sun, Xiaojuan Qi

Therefore, directly learning a mapping function from speech to the entire head image is prone to ambiguity, particularly when using a short video for training.

Image Generation

Count, Decode and Fetch: A New Approach to Handwritten Chinese Character Error Correction

no code implementations30 Jul 2023 Pengfei Hu, Jiefeng Ma, Zhenrong Zhang, Jun Du, Jianshu Zhang

This poses a challenge when dealing with an unseen misspelled character, as the decoder may generate an IDS sequence that matches a seen character instead.

Transfer Learning

HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures

1 code implementation24 Mar 2023 Jiefeng Ma, Jun Du, Pengfei Hu, Zhenrong Zhang, Jianshu Zhang, Huihui Zhu, Cong Liu

Moreover, we proposed an encoder-decoder-based hierarchical document structure parsing system (DSPS) to tackle this problem.

Federated Learning Hyper-Parameter Tuning from a System Perspective

1 code implementation24 Nov 2022 Huanle Zhang, Lei Fu, Mi Zhang, Pengfei Hu, Xiuzhen Cheng, Prasant Mohapatra, Xin Liu

In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements in FL training.

Federated Learning

Learning Audio-Visual embedding for Person Verification in the Wild

no code implementations9 Sep 2022 Peiwen Sun, Shanshan Zhang, Zishan Liu, Yougen Yuan, Taotao Zhang, Honggang Zhang, Pengfei Hu

It has already been observed that audio-visual embedding is more robust than uni-modality embedding for person verification.

Face Verification

High Speed Rotation Estimation with Dynamic Vision Sensors

no code implementations6 Sep 2022 Guangrong Zhao, Yiran Shen, Ning Chen, Pengfei Hu, Lei Liu, Hongkai Wen

By designing a series of signal processing algorithms bespoke for dynamic vision sensing on mobile devices, EV-Tach is able to extract the rotational speed accurately from the event stream produced by dynamic vision sensing on rotary targets.

Vocal Bursts Intensity Prediction

Defensive Patches for Robust Recognition in the Physical World

1 code implementation CVPR 2022 Jiakai Wang, Zixin Yin, Pengfei Hu, Aishan Liu, Renshuai Tao, Haotong Qin, Xianglong Liu, DaCheng Tao

For the generalization against diverse noises, we inject class-specific identifiable patterns into a confined local patch prior, so that defensive patches could preserve more recognizable features towards specific classes, leading models for better recognition under noises.

Membership Inference Attacks Against Recommender Systems

1 code implementation16 Sep 2021 Minxing Zhang, Zhaochun Ren, Zihan Wang, Pengjie Ren, Zhumin Chen, Pengfei Hu, Yang Zhang

In this paper, we make the first attempt on quantifying the privacy leakage of recommender systems through the lens of membership inference.

Recommendation Systems

Dual Synchronous Generator: Inertial Current Source based Grid-Forming Solution for VSC

no code implementations5 Jul 2021 Huanhai Xin, Kehao Zhuang, Pengfei Hu, Yunjie Gu, Ping Ju

Based on dual synchronous idea, a dual synchronous generator (DSG) control is applied in VSC to form inertial current source.

Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection

no code implementations29 Oct 2019 Lingchen Zhao, Shengshan Hu, Qian Wang, Jianlin Jiang, Chao Shen, Xiangyang Luo, Pengfei Hu

Collaborative learning allows multiple clients to train a joint model without sharing their data with each other.

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