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A Matter of Time: Revealing the Structure of Time in Vision-Language Models

Nidham Tekaya, Manuela Waldner, Matthias Zeppelzauer

TL;DR

This work investigates whether large vision-language models implicitly encode temporal information about objects and events. It introduces TIME10k, a temporally annotated image dataset, and a three-pronged framework to probe time, analyze embedding-space structure, and model an explicit timeline from VLM embeddings. The authors demonstrate that temporal information lies on a low-dimensional, non-linear manifold and present two timeline extraction methods—UMAP-based projection and Bézier-curve representation—achieving competitive or superior accuracy with improved efficiency compared to prompt-based baselines. The findings highlight the potential for rapid, scalable temporal reasoning in multimodal models and suggest broader implications for ordinal reasoning tasks in AI.

Abstract

Large-scale vision-language models (VLMs) such as CLIP have gained popularity for their generalizable and expressive multimodal representations. By leveraging large-scale training data with diverse textual metadata, VLMs acquire open-vocabulary capabilities, solving tasks beyond their training scope. This paper investigates the temporal awareness of VLMs, assessing their ability to position visual content in time. We introduce TIME10k, a benchmark dataset of over 10,000 images with temporal ground truth, and evaluate the time-awareness of 37 VLMs by a novel methodology. Our investigation reveals that temporal information is structured along a low-dimensional, non-linear manifold in the VLM embedding space. Based on this insight, we propose methods to derive an explicit ``timeline'' representation from the embedding space. These representations model time and its chronological progression and thereby facilitate temporal reasoning tasks. Our timeline approaches achieve competitive to superior accuracy compared to a prompt-based baseline while being computationally efficient. All code and data are available at https://tekayanidham.github.io/timeline-page/.

A Matter of Time: Revealing the Structure of Time in Vision-Language Models

TL;DR

This work investigates whether large vision-language models implicitly encode temporal information about objects and events. It introduces TIME10k, a temporally annotated image dataset, and a three-pronged framework to probe time, analyze embedding-space structure, and model an explicit timeline from VLM embeddings. The authors demonstrate that temporal information lies on a low-dimensional, non-linear manifold and present two timeline extraction methods—UMAP-based projection and Bézier-curve representation—achieving competitive or superior accuracy with improved efficiency compared to prompt-based baselines. The findings highlight the potential for rapid, scalable temporal reasoning in multimodal models and suggest broader implications for ordinal reasoning tasks in AI.

Abstract

Large-scale vision-language models (VLMs) such as CLIP have gained popularity for their generalizable and expressive multimodal representations. By leveraging large-scale training data with diverse textual metadata, VLMs acquire open-vocabulary capabilities, solving tasks beyond their training scope. This paper investigates the temporal awareness of VLMs, assessing their ability to position visual content in time. We introduce TIME10k, a benchmark dataset of over 10,000 images with temporal ground truth, and evaluate the time-awareness of 37 VLMs by a novel methodology. Our investigation reveals that temporal information is structured along a low-dimensional, non-linear manifold in the VLM embedding space. Based on this insight, we propose methods to derive an explicit ``timeline'' representation from the embedding space. These representations model time and its chronological progression and thereby facilitate temporal reasoning tasks. Our timeline approaches achieve competitive to superior accuracy compared to a prompt-based baseline while being computationally efficient. All code and data are available at https://tekayanidham.github.io/timeline-page/.
Paper Structure (25 sections, 5 equations, 5 figures, 5 tables)

This paper contains 25 sections, 5 equations, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Time Probing: A Baseline for Retrieving Temporal Context (RQ1, RQ2)
  5. Embedding Space Analysis (RQ2)
  6. Chronological Timeline Modelling (RQ3)
  7. UMAP-Based Timeline Representation
  8. Bézier Curve-Based Timeline Representation
  9. Dataset Curation
  10. Experimental Setup
  11. Prompt Design
  12. Vision Language Models
  13. Parameters
  14. Performance Metrics
  15. Evaluation Framework
  16. ...and 10 more sections

Figures (5)

  • Figure 1: Time probing: temporal inference via dot-product similarity. Time embeddings (here for years 1700-2024) are compared with image embeddings. The time embedding with the highest similarity indicates the year of first appearance.
  • Figure 2: Left: The highest score (rank 1) is most often the correct year, followed by the second highest year (rank 2) etc. Right: Ground truth (red diagonal) vs. predicted year for highest (blue) and second-highest (orange) score. Vertical dotted lines represent their difference, which are mostly small.
  • Figure 3: 3D KPCA projection of CLIP time and image embeddings.
  • Figure 4: Fitting a Bézier curve to the time embeddings from CLIP in a 3D projection space. (a) Time embeddings coloured by year with selected control points marked with diamonds. (b) Fitting the Bézier curve. (c) Mapping points to the Bézier curve to obtain a 1D timeline representation.
  • Figure 5: MAE per dimension for (EVA-)CLIP using KPCA.