VindLU: A Recipe for Effective Video-and-Language Pretraining

Author:

Cheng Feng1,Wang Xizi2,Lei Jie1,Crandall David2,Bansal Mohit1,Bertasius Gedas1

Affiliation:

1. UNC Chapel Hill

2. Indiana University

Funder

Sony Faculty Innovation award

Lilly Endowment Inc.

Indiana University Pervasive Technology Institute

NC State University

NSF-AI Institute

Publisher

IEEE

Reference81 articles.

1. An Empirical Study of Training End-to-End Vision-and-Language Transformers

2. Omnivl: One foundation model for image-language and video-language tasks;wang;ArXiv Preprint,2022

3. An image is worth 16x16 words: Transformers for image recognition at scale;dosovitskiy;ArXiv Preprint,2020

4. Reconstruction Network for Video Captioning

5. Masked autoencoders as spatiotemporal learners;feichtenhofer;ArXiv Preprint,2022

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