Domain-robust VQA with diverse datasets and methods but no target labels

Author:

Zhang Mingda,Maidment Tristan,Diab Ahmad,Kovashka Adriana,Hwa Rebecca

Funder

National Science Foundation

Publisher

IEEE

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Cross3DVG: Cross-Dataset 3D Visual Grounding on Different RGB-D Scans;2024 International Conference on 3D Vision (3DV);2024-03-18

2. Benchmarking Out-of-Distribution Detection in Visual Question Answering;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

3. Multi-modal Domain Adaptation for Text Visual Question Answering Tasks;2023 International Conference on Digital Image Computing: Techniques and Applications (DICTA);2023-11-28

4. Multi-Domain Lifelong Visual Question Answering via Self-Critical Distillation;Proceedings of the 31st ACM International Conference on Multimedia;2023-10-26

5. Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images!;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

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