Towards Models that Can See and Read-Reference-Cited by-同舟云学术

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Towards Models that Can See and Read

Published:2023-10-01 Issue: Volume:34 Page:21661-21671
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
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Author:

Ganz Roy¹,Nuriel Oren²,Aberdam Aviad²,Kittenplon Yair²,Mazor Shai²,Litman Ron²

Affiliation:

1. Technion,Israel

2. AWS AI Labs

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10377008.pdf?arnumber=10377008

Reference55 articles.

1. Multimodal semi-supervised learning for text recognition;Aberdam,2022

2. nocaps: novel object captioning at scale

3. Flamingo: a visual language model for few-shot learning;Alayrac,2022

4. VQA: Visual Question Answering

5. DocFormer: End-to-End Transformer for Document Understanding

Cited by 4 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. Scene text visual question answering by using YOLO and STN;International Journal of Speech Technology;2024-01-03

2. FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

3. CLIPAG: Towards Generator-Free Text-to-Image Generation;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

4. PreSTU: Pre-Training for Scene-Text Understanding;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

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