We present HERO, a Hierarchical EncodeR for Omni-representation learning, for large-scale video+language pre-training . HERO encodes multimodal inputs in a hierarchical fashion , where local textual 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. Besides 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. Different from previous work that mostly focused on cooking or narrated instructional videos, HERO is jointly trained on HowTo100M and large-scale TV show datasets to learn complex social scenes, dynamics backdrop transitions and multi-character interactions. Extensive experiments demonstrate that HERO achieves new state of the art on both text-based video moment retrieval and video question answering tasks across different domains  .
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 .
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Hierarchical EncodeR for Omni-representation learning, for
novel framework for
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pre-training
omni-representation learning
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fashion
structure
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textual
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Besides
In addition to
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Different from previous work that mostly focused on cooking or narrated instructional videos,
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show datasets to learn complex social scenes, dynamics backdrop transitions and
datasets to gain deep understanding of complex social dynamics with
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Extensive
Comprehensive
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both text-based video moment retrieval and video question answering
multiple benchmarks over Text-based Video/Video-moment Retrieval, Video Question Answering (QA), Video-and-language Inference and Video Captioning
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. We also introduce two new challenging benchmarks How2QA and How2R for Video QA and Retrieval, collected from diverse video content over multimodalities