Qiaosi Yi

Orcid: 0000-0001-9720-4322

According to our database1, Qiaosi Yi authored at least 16 papers between 2019 and 2023.

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

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2023
Frequency Learning via Multi-Scale Fourier Transformer for MRI Reconstruction.
IEEE J. Biomed. Health Informatics, November, 2023

Textual Prompt Guided Image Restoration.
CoRR, 2023

2022
Efficient and Accurate Multi-Scale Topological Network for Single Image Dehazing.
IEEE Trans. Multim., 2022

Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd Counting.
CoRR, 2022


MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report.
Proceedings of the Computer Vision - ECCV 2022 Workshops, 2022

Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021
MSNet: A novel end-to-end single image dehazing network with multiple inter-scale dense skip-connections.
IET Image Process., 2021

Contrastive Learning for Local and Global Learning MRI Reconstruction.
CoRR, 2021

Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background.
CoRR, 2021

Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation.
Proceedings of the MM '21: ACM Multimedia Conference, Virtual Event, China, October 20, 2021

Structure-Preserving Deraining with Residue Channel Prior Guidance.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
Cumulative Rain Density Sensing Network for Single Image Derain.
IEEE Signal Process. Lett., 2020

Disentangle Perceptual Learning through Online Contrastive Learning.
CoRR, 2020

Progressive Back-Traced Dehazing Network Based on Multi-Resolution Recurrent Reconstruction.
IEEE Access, 2020

2019
Static Crowd Scene Analysis via Deep Network with Multi-branch Dilated Convolution Blocks.
Proceedings of the International Joint Conference on Neural Networks, 2019


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