Papers › ActBERT: Learning Global-Local Video-Text Representations

ActBERT: Learning Global-Local Video-Text Representations

14 Nov 2020CVPR 2020 6arXiv:2011.07231archive 2025-07-28

Linchao Zhu, Yi Yang

In this paper, we introduce ActBERT for self-supervised learning of joint video-text representations from unlabeled data. First, we leverage global action information to catalyze the mutual interactions between linguistic texts and local regional objects. It uncovers global and local visual clues from paired video sequences and text descriptions for detailed visual and text relation modeling. Second, we introduce an ENtangled Transformer block (ENT) to encode three sources of information, i.e., global actions, local regional objects, and linguistic descriptions. Global-local correspondences are discovered via judicious clues extraction from contextual information. It enforces the joint videotext representation to be aware of fine-grained objects as well as global human intention. We validate the generalization capability of ActBERT on downstream video-and language tasks, i.e., text-video clip retrieval, video captioning, video question answering, action segmentation, and action step localization. ActBERT significantly outperforms the state-of-the-arts, demonstrating its superiority in video-text representation learning.

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Tasks

Action SegmentationQuestion AnsweringRepresentation LearningRetrievalSelf-Supervised LearningVideo CaptioningVideo Question Answering

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Segmentation COIN ActBERT Frame accuracy 57.0 #8 of 9 Archive leaderboard report

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Methods

AWAREAbsolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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