Learning Fine-Grained Information Alignment for Calibrated Cross-Modal Retrieval-Reference-Cited by-同舟云学术

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Learning Fine-Grained Information Alignment for Calibrated Cross-Modal Retrieval

Published:2024-04-14 Issue: Volume:35 Page:8286-8290
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Dong Jianhua¹,Zhao Shengrong¹,Liang Hu¹

Affiliation:

1. Qilu University of Technology (Shandong Academy of Sciences),Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center,Jinan,China

Funder

Natural Science Foundation of Shandong Province

Qilu University of Technology

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10448127.pdf?arnumber=10448127

Reference20 articles.

1. 400m: Open dataset of clip-filtered 400 million image-text pairs. arxiv 2021;Schuhmann

2. Position-Guided Text Prompt for Vision-Language Pre-Training

3. Coca: Contrastive captioners are image-text foundation models;Yu,2022

4. Mammut: A simple architecture for joint learning for multimodal tasks;Kuo,2023

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

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