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

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.

PDF Abstract EMNLP 2020 PDF EMNLP 2020 Abstract
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Video Question Answering Howto100M-QA Hero w/ pre-training Accuracy 77.75 # 1
Video Question Answering TVQA Hero w/ pre-training Accuracy 74.24 # 2
Video Retrieval TVR Hero w/ pre-training R@1 4.34 # 1
R@10 13.97 # 1
R@100 21.78 # 1

Results from Other Papers


Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Video Corpus Moment Retrieval TVR HERO (Li et al., 2020) R@1 5.13 # 2
R@10 16.26 # 2
R@100 24.56 # 2

Methods