Search Results for author: Yong Tang

Found 12 papers, 2 papers with code

Privileged Prior Information Distillation for Image Matting

no code implementations25 Nov 2022 Cheng Lyu, Jiake Xie, Bo Xu, Cheng Lu, Han Huang, Xin Huang, Ming Wu, Chuang Zhang, Yong Tang

Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance.

Image Matting

Label Mask AutoEncoder(L-MAE): A Pure Transformer Method to Augment Semantic Segmentation Datasets

no code implementations21 Nov 2022 Jiaru Jia, Mingzhe Liu, Jiake Xie, Xin Chen, Aiqing Yang, Xin Jiang, Hong Zhang, Yong Tang

Semantic segmentation models based on the conventional neural network can achieve remarkable performance in such tasks, while the dataset is crucial to the training model process.

Semi-Supervised Semantic Segmentation

Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph Learning

no code implementations9 Sep 2022 Si-Guo Fang, Dong Huang, Xiao-Sha Cai, Chang-Dong Wang, Chaobo He, Yong Tang

By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size.

Graph Learning

Situational Perception Guided Image Matting

no code implementations20 Apr 2022 Bo Xu, Jiake Xie, Han Huang, Ziwen Li, Cheng Lu, Yong Tang, Yandong Guo

In this paper, we propose a Situational Perception Guided Image Matting (SPG-IM) method that mitigates subjective bias of matting annotations and captures sufficient situational perception information for better global saliency distilled from the visual-to-textual task.

Association Image Matting

Joint Multi-view Unsupervised Feature Selection and Graph Learning

no code implementations18 Apr 2022 Si-Guo Fang, Dong Huang, Chang-Dong Wang, Yong Tang

Second, they often learn the similarity structure by either global structure learning or local structure learning, lacking the capability of graph learning with both global and local structural awareness.

Graph Learning

GANet: Glyph-Attention Network for Few-Shot Font Generation

no code implementations29 Sep 2021 Mingtao Guo, Wei Xiong, Zheng Wang, Yong Tang, Ting Wu

Font generation is a valuable but challenging task, it is time consuming and costly to design font libraries which cover all glyphs with various styles.

Font Generation

ExamGAN and Twin-ExamGAN for Exam Script Generation

no code implementations22 Aug 2021 Zhengyang Wu, Ke Deng, Judy Qiu, Yong Tang

There are opportunities to further improve the quality of generated exam scripts in various aspects.


Tripartite Information Mining and Integration for Image Matting

1 code implementation ICCV 2021 Yuhao Liu, Jiake Xie, Xiao Shi, Yu Qiao, Yujie Huang, Yong Tang, Xin Yang

Regarding the nature of image matting, most researches have focused on solutions for transition regions.

Image Matting

Modal-aware Features for Multimodal Hashing

no code implementations19 Nov 2019 Haien Zeng, Hanjiang Lai, Hanlu Chu, Yong Tang, Jian Yin

The modal-aware operation consists of a kernel network and an attention network.


An Interactive Insight Identification and Annotation Framework for Power Grid Pixel Maps using DenseU-Hierarchical VAE

no code implementations22 May 2019 Tianye Zhang, Haozhe Feng, Zexian Chen, Can Wang, Yanhao Huang, Yong Tang, Wei Chen

Insights in power grid pixel maps (PGPMs) refer to important facility operating states and unexpected changes in the power grid.

Dense Adaptive Cascade Forest: A Self Adaptive Deep Ensemble for Classification Problems

no code implementations29 Apr 2018 Haiyang Wang, Yong Tang, Ziyang Jia, Fei Ye

Second, our model connects each layer to the subsequent ones in a feed-forward fashion, which enhances the capability of the model to resist performance degeneration.

Ensemble Learning General Classification

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