Video Time: Properties, Encoders and Evaluation
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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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- 2021FrameExit: Conditional Early Exiting for Efficient Video Recognitioncited by 59
- 2023VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Modelscited by 55
- 2025Seeing the Arrow of Time in Large Multimodal Modelscited by 28
- 2022Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foregroundcited by 26
- 2022A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Informationcited by 24
- 2022How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?cited by 23
- 2024Understanding Video Transformers via Universal Concept Discoverycited by 22
- 2025Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Videocited by 20
- 2026Interpreting Physics in Video World Modelscited by 18
- 2022Is Appearance Free Action Recognition Possible?cited by 16
- 2024Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text Retrievalcited by 12
- 2025Chirality in Action: Time-Aware Video Representation Learning by Latent Straighteningcited by 10
7 more
- 2021Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognitioncited by 7
- 2022Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networkscited by 7
- 2025SEVERE++: Evaluating Benchmark Sensitivity in Generalization of Video Representation Learningcited by 4
- 2025Order Matters: On Parameter-Efficient Image-to-Video Probing for Recognizing Nearly Symmetric Actionscited by 3
- 2021NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognitioncited by 1
- 2026Seeing Fast and Slow: Learning the Flow of Time in Videoscited by 1
- 2026What, Where, and How: Probing Spatiotemporal Representations in Video Foundation Modelscited by 0
- 2026What, Where, and How: Probing Spatiotemporal Representations in Video Foundation Models1 Sep 2026
- 2026Seeing Fast and Slow: Learning the Flow of Time in Videos23 Apr 2026
- 2026Interpreting Physics in Video World Models4 Feb 2026
- 2025Chirality in Action: Time-Aware Video Representation Learning by Latent Straightening10 Sep 2025
- 2025Seeing the Arrow of Time in Large Multimodal Models3 Jun 2025
- 2025Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Video28 Apr 2025
- 2025SEVERE++: Evaluating Benchmark Sensitivity in Generalization of Video Representation Learning8 Apr 2025
- 2025Order Matters: On Parameter-Efficient Image-to-Video Probing for Recognizing Nearly Symmetric Actions31 Mar 2025
- 2024Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text Retrieval26 Dec 2024
- 2024Understanding Video Transformers via Universal Concept Discovery19 Jan 2024
- 2023VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models29 Nov 2023
- 2022Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foreground23 Nov 2022
7 more
- 2022Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks3 Nov 2022
- 2022Is Appearance Free Action Recognition Possible?13 Jul 2022
- 2022A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information6 Jun 2022
- 2022How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?27 Mar 2022
- 2021Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition22 Dec 2021
- 2021FrameExit: Conditional Early Exiting for Efficient Video Recognition27 Apr 2021
- 2021NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition17 Mar 2021
- 2025Seeing the Arrow of Time in Large Multimodal Modelscited by 28
- 2025Seeing the Arrow of Time in Large Multimodal Models3 Jun 2025
- 2026Interpreting Physics in Video World Modelscited by 18
- 2026Interpreting Physics in Video World Models4 Feb 2026
- 2021FrameExit: Conditional Early Exiting for Efficient Video Recognitioncited by 59
- 2023VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Modelscited by 55
- 2022Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foregroundcited by 26
- 2022A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Informationcited by 24
- 2022How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?cited by 23
- 2024Understanding Video Transformers via Universal Concept Discoverycited by 22
- 2025Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Videocited by 20
- 2022Is Appearance Free Action Recognition Possible?cited by 16
- 2024Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text Retrievalcited by 12
- 2025Chirality in Action: Time-Aware Video Representation Learning by Latent Straighteningcited by 10
- 2021Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognitioncited by 7
- 2022Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networkscited by 7
12 most cited of 17 All 17 (JSON)
- 2026What, Where, and How: Probing Spatiotemporal Representations in Video Foundation Models1 Sep 2026
- 2026Seeing Fast and Slow: Learning the Flow of Time in Videos23 Apr 2026
- 2025Chirality in Action: Time-Aware Video Representation Learning by Latent Straightening10 Sep 2025
- 2025Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Video28 Apr 2025
- 2025SEVERE++: Evaluating Benchmark Sensitivity in Generalization of Video Representation Learning8 Apr 2025
- 2025Order Matters: On Parameter-Efficient Image-to-Video Probing for Recognizing Nearly Symmetric Actions31 Mar 2025
- 2024Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text Retrieval26 Dec 2024
- 2024Understanding Video Transformers via Universal Concept Discovery19 Jan 2024
- 2023VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models29 Nov 2023
- 2022Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foreground23 Nov 2022
- 2022Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks3 Nov 2022
- 2022Is Appearance Free Action Recognition Possible?13 Jul 2022
12 newest of 17 All 17 (JSON)