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Learning Video Representations using Contrastive Bidirectional Transformer

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arxiv 1906.05743 v2 pith:OCRYD62G submitted 2019-06-13 cs.LG cs.CVstat.ML

Chen Sun , Fabien Baradel , Kevin Murphy , Cordelia Schmid This is my paper · ORCID
classification cs.LGcs.CVstat.ML
keywords sequencesvideocontrastivefeatureslearningrepresentationsapproachautomatic
verification ladder T0 review T1 audit T2 compute T3 formal

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This paper proposes a self-supervised learning approach for video features that results in significantly improved performance on downstream tasks (such as video classification, captioning and segmentation) compared to existing methods. Our method extends the BERT model for text sequences to the case of sequences of real-valued feature vectors, by replacing the softmax loss with noise contrastive estimation (NCE). We also show how to learn representations from sequences of visual features and sequences of words derived from ASR (automatic speech recognition), and show that such cross-modal training (when possible) helps even more.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CLIP-CC-Bench: Evaluating Paragraph-Level Video Descriptions in Video-Language Models

    cs.CV 2026-08 conditional novelty 6.0 of 10

    CLIP-CC-Bench is a 200-clip benchmark with expert paragraph references that ranks 17 video-language models via an ensemble of five embedding-based semantic judges.

  2. VL-BERT: Pre-training of Generic Visual-Linguistic Representations

    cs.CV 2019-08 accept novelty 6.0 of 10

    VL-BERT pre-trains a single-stream Transformer on image captions and text, and the resulting representation improves VCR, VQA, and RefCOCO+ benchmarks.

  3. Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

    cs.LG 2025-06 reject novelty 5.0 of 10

    TimesCLIP aligns image-based and text-based views of the same time series via contrastive learning to improve forecasting accuracy on several benchmarks, but the full multimodal model is not used on two of the six lon...

  4. Reinforcement Learning meets Masked Video Modeling : Trajectory-Guided Adaptive Token Selection

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A trajectory-attention token sampler trained jointly with a masked video autoencoder via PPO improves downstream action recognition accuracy over random- and activity-based masking baselines.

  5. Kronecker Mask and Interpretive Prompts are Language-Action Video Learners

    cs.CV 2025-02 conditional novelty 5.0 of 10

    CLAVER adds a cross-frame temporal attention mask (Kronecker mask) and LLM-generated interpretive action prompts to CLIP, improving video action recognition.

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