Search Results for author: Shijie Hao

Found 8 papers, 4 papers with code

Decoupled Low-light Image Enhancement

1 code implementation29 Nov 2021 Shijie Hao, Xu Han, Yanrong Guo, Meng Wang

On the other hand, since the parameter matrix learned from the first stage is aware of the lightness distribution and the scene structure, it can be incorporated into the second stage as the complementary information.

Low-Light Image Enhancement

Few-shot Learning with Global Relatedness Decoupled-Distillation

no code implementations12 Jul 2021 Yuan Zhou, Yanrong Guo, Shijie Hao, Richang Hong, ZhengJun Zha, Meng Wang

To overcome these problems, we propose a new Global Relatedness Decoupled-Distillation (GRDD) method using the global category knowledge and the Relatedness Decoupled-Distillation (RDD) strategy.

Few-Shot Learning Metric Learning

Few-shot Partial Multi-view Learning

no code implementations5 May 2021 Yuan Zhou, Yanrong Guo, Shijie Hao, Richang Hong, Jiebo Luo

The challenges of this task are twofold: (1) under the interference of the missing views, it is difficult to overcome the negative impact brought by data scarcity; (2) the limited number of data exacerbates information scarcity, thereby making it harder to address the view-missing problem.


Positive Sample Propagation along the Audio-Visual Event Line

2 code implementations CVPR 2021 Jinxing Zhou, Liang Zheng, Yiran Zhong, Shijie Hao, Meng Wang

To encourage the network to extract high correlated features for positive samples, a new audio-visual pair similarity loss is proposed.

audio-visual event localization

A Survey on Large-scale Machine Learning

1 code implementation10 Aug 2020 Meng Wang, Weijie Fu, Xiangnan He, Shijie Hao, Xindong Wu

Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems.

Recommendation Systems

Distribution-Guided Local Explanation for Black-Box Classifiers

no code implementations25 Sep 2019 Weijie Fu, Meng Wang, Mengnan Du, Ninghao Liu, Shijie Hao, Xia Hu

Existing local explanation methods provide an explanation for each decision of black-box classifiers, in the form of relevance scores of features according to their contributions.

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