mlsanityData to 7 Oct 2026

Video Time: Properties, Encoders and Evaluation

arXiv 1807.06980 ↗First posted 18 Jul 2018cs.CVcited by 19

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Abstract (arXiv)

Time-aware encoding of frame sequences in a video is a fundamental problem in video understanding. While many attempted to model time in videos, an explicit study on quantifying video time is missing. To fill this lacuna, we aim to evaluate video time explicitly. We describe three properties of video time, namely a) temporal asymmetry, b)temporal continuity and c) temporal causality. Based on each we formulate a task able to quantify the associated property. This allows assessing the effectiveness of modern video encoders, like C3D and LSTM, in their ability to model time. Our analysis provides insights about existing encoders while also leading us to propose a new video time encoder, which is better suited for the video time recognition tasks than C3D and LSTM. We believe the proposed meta-analysis can provide a reasonable baseline to assess video time encoders on equal grounds on a set of temporal-aware tasks.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jul 2018.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 1; 2024: 2; 2025: 5; 2026: 3.

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  1. 2021FrameExit: Conditional Early Exiting for Efficient Video Recognitioncited by 59
  2. 2023VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Modelscited by 55
  3. 2025Seeing the Arrow of Time in Large Multimodal Modelscited by 28
  4. 2022Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foregroundcited by 26
  5. 2022A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Informationcited by 24
  6. 2022How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?cited by 23
  7. 2024Understanding Video Transformers via Universal Concept Discoverycited by 22
  8. 2025Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Videocited by 20
  9. 2026Interpreting Physics in Video World Modelscited by 18
  10. 2022Is Appearance Free Action Recognition Possible?cited by 16
  11. 2024Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text Retrievalcited by 12
  12. 2025Chirality in Action: Time-Aware Video Representation Learning by Latent Straighteningcited by 10
7 more
  1. 2021Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognitioncited by 7
  2. 2022Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networkscited by 7
  3. 2025SEVERE++: Evaluating Benchmark Sensitivity in Generalization of Video Representation Learningcited by 4
  4. 2025Order Matters: On Parameter-Efficient Image-to-Video Probing for Recognizing Nearly Symmetric Actionscited by 3
  5. 2021NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognitioncited by 1
  6. 2026Seeing Fast and Slow: Learning the Flow of Time in Videoscited by 1
  7. 2026What, Where, and How: Probing Spatiotemporal Representations in Video Foundation Modelscited by 0