Robust visual question answering via semantic cross modal augmentation-Reference-Cited by-同舟云学术

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Robust visual question answering via semantic cross modal augmentation

Published:2024-01 Issue: Volume:238 Page:103862
ISSN:1077-3142
Container-title:Computer Vision and Image Understanding
language:en
Short-container-title:Computer Vision and Image Understanding

Author:

Mashrur Akib^ORCID,Luo Wei,Zaidi Nayyar A.,Robles-Kelly Antonio

Publisher

Elsevier BV

Subject

Computer Vision and Pattern Recognition,Signal Processing,Software

Reference45 articles.

1. Analyzing the behavior of visual question answering models;Agrawal,2016

2. Don’t just assume; look and answer: Overcoming priors for visual question answering;Agrawal,2018

3. VQA: Visual question answering;Antol,2015

4. Pitfalls of in-domain uncertainty estimation and ensembling in deep learning;Ashukha,2020

5. Bhattacharya, N., Li, Q., Gurari, D., 2019. Why does a visual question have different answers?. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4271–4280.

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1. Cascade transformers with dynamic attention for video question answering;Computer Vision and Image Understanding;2024-05

2. Enhanced Visual Question Answering System Using DenseNet;2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS);2024-04-18

3. Viclevr: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in Vietnamese;2023

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