Qiong Wu

Affiliations:
  • Xiamen University, Institute of Artificial Intelligence and the Media, Analytics and Computing Laboratory, School of Informatics, China


According to our database1, Qiong Wu authored at least 14 papers between 2018 and 2024.

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

Timeline

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Links

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Bibliography

2024
Bilateral Knowledge Interaction Network for Referring Image Segmentation.
IEEE Trans. Multim., 2024

Routing Experts: Learning to Route Dynamic Experts in Multi-modal Large Language Models.
CoRR, 2024

Not All Attention is Needed: Parameter and Computation Efficient Transfer Learning for Multi-modal Large Language Models.
CoRR, 2024

2023
Approximated Prompt Tuning for Vision-Language Pre-trained Models.
CoRR, 2023

Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting.
CoRR, 2023

Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning.
CoRR, 2023

Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Disentangling Task-Oriented Representations for Unsupervised Domain Adaptation.
IEEE Trans. Image Process., 2022

CycleTrans: Learning Neutral yet Discriminative Features for Visible-Infrared Person Re-Identification.
CoRR, 2022

2021
Deep adversarial data augmentation with attribute guided for person re-identification.
Signal Image Video Process., 2021

Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Learning Task-oriented Disentangled Representations for Unsupervised Domain Adaptation.
CoRR, 2020

2019
Video-based Person Re-identification with Two-stream Convolutional Network and Co-attentive Snippet Embedding.
CoRR, 2019

2018
Cross-Modality Person Re-Identification with Generative Adversarial Training.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018


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