Xiyao Liu

Orcid: 0000-0002-9978-1286

Affiliations:
  • Tianjin University, Tianjin, China


According to our database1, Xiyao Liu authored at least 14 papers between 2020 and 2024.

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

Timeline

Legend:

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Bibliography

2024
Semantic-Aware Dynamic Generation Networks for Few-Shot Human-Object Interaction Recognition.
IEEE Trans. Neural Networks Learn. Syst., September, 2024

Relevance equilibrium network for cross-domain few-shot learning.
Int. J. Multim. Inf. Retr., June, 2024

On the Approximation Risk of Few-Shot Class-Incremental Learning.
Proceedings of the Computer Vision - ECCV 2024, 2024

2023
Dual Distillation Discriminator Networks for Domain Adaptive Few-Shot Learning.
Neural Networks, August, 2023

Self-taught cross-domain few-shot learning with weakly supervised object localization and task-decomposition.
Knowl. Based Syst., April, 2023

Memorizing Complementation Network for Few-Shot Class-Incremental Learning.
IEEE Trans. Image Process., 2023

2022
Task-Oriented High-Order Context Graph Networks for Few-Shot Human-Object Interaction Recognition.
IEEE Trans. Syst. Man Cybern. Syst., 2022

Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning.
IEEE Trans. Image Process., 2022

DGIG-Net: Dynamic Graph-in-Graph Networks for Few-Shot Human-Object Interaction.
IEEE Trans. Cybern., 2022

Meta hyperbolic networks for zero-shot learning.
Neurocomputing, 2022

Teachers cooperation: team-knowledge distillation for multiple cross-domain few-shot learning.
Frontiers Comput. Sci., 2022

2021
Few-Shot Human-Object Interaction Recognition With Semantic-Guided Attentive Prototypes Network.
IEEE Trans. Image Process., 2021

Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition.
CoRR, 2021

2020
SGAP-Net: Semantic-Guided Attentive Prototypes Network for Few-Shot Human-Object Interaction Recognition.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020


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