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HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
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We present HERO, a novel framework for large-scale video+language omnirepresentation 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. 1
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“As demonstrated in Figure 2 in [192], the multimodal Transformer models have another architecture taxonomy based on the computational size of the components.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [192]
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“One simplifying is processing the visual input in convolution-free manner, e.g., E2E-VLP [271], ViLT [192].”
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Metadata
- Authors
- Li, L., Chen, Y.-C., Cheng, Y., Gan, Z., Yu, L., & Liu, J.
- Affiliations
- Microsoft (United States) (US)
- Venue
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2020
- Pages
- 2046-2065
- OpenAlex
- W3105232955
- Cited by
- 408
- References
- 79
- Citations by year
- 2026: 17 · 2025: 49 · 2024: 67 · 2023: 129 · 2022: 87 · 2021: 52 · 2020: 7
- FWCI
- 21.21
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Keywords
- multimodal representation learning, hierarchical encoder, cross-modal transformer, temporal transformer, video question answering, video retrieval, video captioning, video moment retrieval
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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