Ni Zhang

Orcid: 0000-0002-6645-3366

Affiliations:
  • Xi'an Jiaotong University, School of Computer Science and Technology, Xi'an, China
  • Northwestern Polytechnical University, School of Automation, Xi'an, China (PhD 2024)


According to our database1, Ni Zhang authored at least 12 papers between 2020 and 2025.

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

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2025
Visual-guided human-object interaction detection.
Vis. Intell., 2025

Exploring Triple Knowledge Cues for Zero-Shot Human-Object Interaction Detection.
Proceedings of the 2025 IEEE International Conference on Acoustics, 2025

Density-aware and Depth-aware Visual Representation for Zero-Shot Object Counting.
Proceedings of the 2025 IEEE International Conference on Acoustics, 2025

2024
VST++: Efficient and Stronger Visual Saliency Transformer.
IEEE Trans. Pattern Anal. Mach. Intell., November, 2024

CADC++: Advanced Consensus-Aware Dynamic Convolution for Co-Salient Object Detection.
IEEE Trans. Pattern Anal. Mach. Intell., May, 2024

2023
Face De-Occlusion With Deep Cascade Guidance Learning.
IEEE Trans. Multim., 2023

Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Learning Implicit Class Knowledge for RGB-D Co-Salient Object Detection With Transformers.
IEEE Trans. Image Process., 2022

Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

2021
Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Visual Saliency Transformer.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
Learning Selective Self-Mutual Attention for RGB-D Saliency Detection.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020


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