Chengxi Zeng
Orcid: 0000-0002-0872-2054
According to our database1,
Chengxi Zeng authored at least 17 papers
between 2022 and 2026.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2026
CoRR, April, 2026
The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview.
CoRR, April, 2026
The Fourth Challenge on Image Super-Resolution (⨉4) at NTIRE 2026: Benchmark Results and Method Overview.
CoRR, April, 2026
NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results.
CoRR, April, 2026
The Second Challenge on Real-World Face Restoration at NTIRE 2026: Methods and Results.
CoRR, April, 2026
SAM3-LiteText: An Anatomical Study of the SAM3 Text Encoder for Efficient Vision-Language Segmentation.
Proceedings of the 2026 International Conference on Multimedia Retrieval, 2026
Proceedings of the 2026 International Conference on Multimedia Retrieval, 2026
2025
EfficientSAM3: Progressive Hierarchical Distillation for Video Concept Segmentation from SAM1, 2, and 3.
CoRR, November, 2025
CoRR, April, 2025
Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations.
CoRR, January, 2025
AIM 2025 Challenge on Robust Offline Video Super-Resolution: Dataset, Methods and Results.
Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV 2025, 2025
C2D-ISR: Optimizing Attention-Based Image Super-Resolution from Continuous to Discrete Scales.
Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV 2025, 2025
2024
CoRR, 2024
CoRR, 2024
2023
Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation.
Proceedings of the IEEE International Conference on Image Processing, 2023
2022
Video-TransUNet: temporally blended vision transformer for CT VFSS instance segmentation.
Proceedings of the Fifteenth International Conference on Machine Vision, 2022