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

2025-10-22

AI Summary

This work investigates the capability of large-scale vision-language models (VLMs) to understand and localize temporal information in images. Addressing the limitations of existing approaches—which rely heavily on hand-crafted prompts and lack explicit temporal modeling—we discover, for the first time, a low-dimensional nonlinear temporal manifold embedded within VLM visual representation spaces. Leveraging this insight, we propose a prompt-free “timeline” representation construction method: it extracts computationally tractable temporal structure from visual embeddings of 37 mainstream VLMs via nonlinear dimensionality reduction. Evaluated on our newly curated TIME10k benchmark, the method achieves performance on par with or surpassing prompt-based baselines in temporal localization tasks, while significantly accelerating inference. To foster reproducibility and further research, both code and data are publicly released.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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/.

Problem

• Investigating temporal awareness in vision-language models
• Evaluating models' ability to position visual content in time
• Developing timeline representations for temporal reasoning tasks

Innovation

• Benchmark dataset TIME10k evaluates temporal awareness
• Low-dimensional manifold structures time in embeddings
• Timeline representation enables efficient temporal reasoning