Zhangyong Tang

Orcid: 0000-0001-8187-9384

According to our database1, Zhangyong Tang authored at least 16 papers between 2018 and 2025.

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Bibliography

2025
TENet: Targetness entanglement incorporating with multi-scale pooling and mutually-guided fusion for RGB-E object tracking.
Neural Networks, 2025

TextFusion: Unveiling the power of textual semantics for controllable image fusion.
Inf. Fusion, 2025

2024
Multi-Level Fusion for Robust RGBT Tracking via Enhanced Thermal Representation.
ACM Trans. Multim. Comput. Commun. Appl., October, 2024

UniMod1K: Towards a More Universal Large-Scale Dataset and Benchmark for Multi-modal Learning.
Int. J. Comput. Vis., August, 2024

Latency Minimization for IRS-enhanced Wideband MEC Networks with Practical Reflection Model.
CoRR, 2024

Revisiting RGBT Tracking Benchmarks from the Perspective of Modality Validity: A New Benchmark, Problem, and Method.
CoRR, 2024

Generative-Based Fusion Mechanism for Multi-Modal Tracking.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Exploring fusion strategies for accurate RGBT visual object tracking.
Inf. Fusion, November, 2023

The First Visual Object Tracking Segmentation VOTS2023 Challenge Results.
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Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

RGBD1K: A Large-Scale Dataset and Benchmark for RGB-D Object Tracking.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
A Survey for Deep RGBT Tracking.
CoRR, 2022

Temporal Aggregation for Adaptive RGBT Tracking.
CoRR, 2022

The Tenth Visual Object Tracking VOT2022 Challenge Results.
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Proceedings of the Computer Vision - ECCV 2022 Workshops, 2022

2021
The Ninth Visual Object Tracking VOT2021 Challenge Results.
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Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

2020
The Eighth Visual Object Tracking VOT2020 Challenge Results.
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Proceedings of the Computer Vision - ECCV 2020 Workshops, 2020

2018
Time Context-Aware IPTV Program Recommendation Based on Tensor Learning.
Proceedings of the IEEE Global Communications Conference, 2018


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