HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training

Linjie Li, Yen-Chun Chen, Yu Cheng, Zhe Gan, Licheng Yu, Jingjing Liu


Abstract
We present HERO, a novel framework for large-scale video+language omni-representation learning. HERO encodes multimodal inputs in a hierarchical structure, where local context of a video frame is captured by a Cross-modal Transformer via multimodal fusion, and global video context is captured by a Temporal Transformer. In addition to standard Masked Language Modeling (MLM) and Masked Frame Modeling (MFM) objectives, we design two new pre-training tasks: (i) Video-Subtitle Matching (VSM), where the model predicts both global and local temporal alignment; and (ii) Frame Order Modeling (FOM), where the model predicts the right order of shuffled video frames. HERO is jointly trained on HowTo100M and large-scale TV datasets to gain deep understanding of complex social dynamics with multi-character interactions. Comprehensive experiments demonstrate that HERO achieves new state of the art on multiple benchmarks over Text-based Video/Video-moment Retrieval, Video Question Answering (QA), Video-and-language Inference and Video Captioning tasks across different domains. We also introduce two new challenging benchmarks How2QA and How2R for Video QA and Retrieval, collected from diverse video content over multimodalities.
Anthology ID:
2020.emnlp-main.161
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2046–2065
Language:
URL:
https://aclanthology.org/2020.emnlp-main.161
DOI:
10.18653/v1/2020.emnlp-main.161
Bibkey:
Cite (ACL):
Linjie Li, Yen-Chun Chen, Yu Cheng, Zhe Gan, Licheng Yu, and Jingjing Liu. 2020. HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2046–2065, Online. Association for Computational Linguistics.
Cite (Informal):
HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training (Li et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.161.pdf
Video:
 https://slideslive.com/38939211
Code
 linjieli222/HERO
Data
How2QAHow2RDiDeMoHowTo100MTVCTVQATVQA+TVRViolinYouCook2