Search Results for author: Fei Ding

Found 7 papers, 3 papers with code

Multi-shot Temporal Event Localization: a Benchmark

1 code implementation CVPR 2021 Xiaolong Liu, Yao Hu, Song Bai, Fei Ding, Xiang Bai, Philip H. S. Torr

Current developments in temporal event or action localization usually target actions captured by a single camera.

Ranked #2 on Temporal Action Localization on THUMOS’14 (using extra training data)

Temporal Action Localization

Multi-level Knowledge Distillation via Knowledge Alignment and Correlation

1 code implementation1 Dec 2020 Fei Ding, Yin Yang, Hongxin Hu, Venkat Krovi, Feng Luo

While it is important to transfer the full knowledge from teacher to student, we introduce the Multi-level Knowledge Distillation (MLKD) by effectively considering both knowledge alignment and correlation.

Contrastive Learning Knowledge Distillation +2

Model-Free Voltage Regulation of Unbalanced Distribution Network Based on Surrogate Model and Deep Reinforcement Learning

no code implementations24 Jun 2020 Di Cao, Junbo Zhao, Weihao Hu, Fei Ding, Qi Huang, Zhe Chen, Frede Blaabjerg

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage controls, but this is difficult to obtain in practice.

Decision Making

Distributed Voltage Regulation of Active Distribution System Based on Enhanced Multi-agent Deep Reinforcement Learning

no code implementations31 May 2020 Di Cao, Junbo Zhao, Weihao Hu, Fei Ding, Qi Huang, Zhe Chen

This paper proposes a data-driven distributed voltage control approach based on the spectrum clustering and the enhanced multi-agent deep reinforcement learning (MADRL) algorithm.

Hierarchical Attention Networks for Medical Image Segmentation

no code implementations20 Nov 2019 Fei Ding, Gang Yang, Jinlu Liu, Jun Wu, Dayong Ding, Jie Xv, Gangwei Cheng, Xirong Li

Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method, namely Hierarchical Attention Network (HANet), to address the problem of medical image segmentation.

Medical Image Segmentation

Double cycle-consistent generative adversarial network for unsupervised conditional generation

no code implementations13 Nov 2019 Fei Ding, Feng Luo, Yin Yang

We enforce the encoder and the generator of GAN to form an encoder-generator pair in addition to the generator-encoder pair, which enables us to avoid the low-diversity generation and the triviality of latent features.

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