Ping Wang

Orcid: 0000-0003-3808-0387

Affiliations:
  • Tianjin University, Tianjin, China


According to our database1, Ping Wang authored at least 14 papers between 2014 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2025
Unleashing Degradation-Carrying Features in Symmetric U-Net: Simpler and Stronger Baselines for All-in-One Image Restoration.
CoRR, December, 2025

SlimHead: Rethinking the Efficiency Bottleneck in Dense Object Detection.
Proceedings of the Pattern Recognition and Computer Vision - 8th Chinese Conference, 2025

2024
Zone Evaluation: Revealing Spatial Bias in Object Detection.
IEEE Trans. Pattern Anal. Mach. Intell., December, 2024

2023
Localization Distillation for Object Detection.
IEEE Trans. Pattern Anal. Mach. Intell., August, 2023

2022
Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation.
IEEE Trans. Cybern., 2022

Localization Distillation for Dense Object Detection.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
A Recursive Regularization Based Feature Selection Framework for Hierarchical Classification.
IEEE Trans. Knowl. Data Eng., 2021

Localization Distillation for Object Detection.
CoRR, 2021

2020
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Fuzzy Rough Set Based Feature Selection for Large-Scale Hierarchical Classification.
IEEE Trans. Fuzzy Syst., 2019

Dual Recursive Network for Fast Image Deraining.
Proceedings of the 2019 IEEE International Conference on Image Processing, 2019

2017
Hierarchical Feature Selection with Recursive Regularization.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

2016
Cost-sensitive feature selection based on adaptive neighborhood granularity with multi-level confidence.
Inf. Sci., 2016

2014
Support Vector Guided Dictionary Learning.
Proceedings of the Computer Vision - ECCV 2014, 2014


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