timeline-vlm 1.0.5

Temporal inference with Vision-Language Models — predict when an image was taken from its visual content.

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

Official implementation of "A Matter of Time: Revealing the Structure of Time in Vision-Language Models", published at ACM Multimedia 2025 (MM '25).

We investigate the temporal awareness of VLMs, assessing their ability to position visual content in time. We introduce TIME10k, a benchmark of over 10,000 images with temporal ground truth, and evaluate 37 VLMs. We reveal that temporal information is structured along a low-dimensional, non-linear manifold in the VLM embedding space. We propose methods to derive an explicit "timeline" representation using UMAP and Bezier curve approximation, achieving competitive to superior accuracy while being computationally efficient.

Try it now: A live demo is available on Hugging Face Spaces.

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Quick Start

Install

pip install timeline-vlm

For CLIP models, also install:

pip install git+https://github.com/openai/CLIP.git

Predict the year of any image in 3 lines:

from timeline_vlm import TimelinePredictor

predictor = TimelinePredictor('clip-vit-b32').fit_from_precomputed('encodings')
print(predictor.predict('photo.jpg'))  # -> 1972

Or from the command line:

timeline-vlm predict photo.jpg
timeline-vlm predict photo.jpg --model "CLIP ViT-L/14" --method time_probing

List available models:

from timeline_vlm import list_models
print(list_models())  # 37 supported VLMs

# or from CLI
timeline-vlm list-models

No GPU required — precomputed embeddings for CLIP and EVA-CLIP are included.

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Installation

From PyPI (recommended)

pip install timeline-vlm

From source (for development or paper reproduction)

git clone https://github.com/tekayanidham/timeline-vlm.git
cd timeline-vlm
pip install -e .

Optional dependencies

# CLIP models (not on PyPI, must be installed separately)
pip install git+https://github.com/openai/CLIP.git

# OpenCLIP models
pip install timeline-vlm[openclip]

# All optional dependencies (excluding CLIP)
pip install timeline-vlm[all]

# For all 37 models (including EVA-CLIP, ImageBind and ViT-Lens)
bash install_models.sh

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Python API

from timeline_vlm import TimelinePredictor

# Initialize and fit
predictor = TimelinePredictor(
    model='clip-vit-b32',          # Any of the 37 supported models
    method='bezier',               # 'time_probing', 'umap', or 'bezier'
    reduce_dim=None,               # KPCA dimensions (None = original space, 13 = optimal)
    bezier_method='interpolation', # 'interpolation' or 'nearest_neighbor'
)
predictor.fit_from_precomputed('encodings')

# Single prediction
year = predictor.predict('photo.jpg')

# Batch prediction
years = predictor.predict_batch(['img1.jpg', 'img2.jpg', 'img3.jpg'])

# Detailed prediction
details = predictor.predict_with_details('photo.jpg')

# Evaluate on your own data
results = predictor.evaluate(image_embeddings, ground_truth_years)
print(f"MAE: {results['mae']:.2f}, TAI: {results['tai']:.3f}")

See docs/library.md for the full API reference.

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CLI

# Predict
timeline-vlm predict photo.jpg
timeline-vlm predict photos/ --output json --save results.json

# List models
timeline-vlm list-models
timeline-vlm list-models --verbose

# Visualize (1D, 2D, or 3D)
timeline-vlm visualize timeline --model clip-vit-b32 --dim 3 --save timeline.png
timeline-vlm visualize prediction --image photo.jpg --dim 2 --save pred.png

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Reproducing Paper Results

python scripts/reproduce_results.py --table 5                        # Single table
python scripts/reproduce_results.py --table 4 5                      # Multiple tables
python scripts/reproduce_results.py --figure 6                       # Figure 6
python scripts/reproduce_results.py --all                            # Everything
python scripts/run_experiments.py --config configs/full_evaluation.yaml  # Full benchmark

Flag	What it reproduces
--table 1	Time probing MAE & TAI for 37 VLMs (P7)
--table 2	Prompt sensitivity P1-P9
--table 3	Class-wise temporal awareness
--table 4	Chronological ordering quality (KPCA vs UMAP)
--table 5	Method comparison: Time Probing vs UMAP vs 4 Bezier variants
--figure 6	MAE per KPCA dimension (optimal S=13)

See docs/reproducing_results.md for the full step-by-step guide.

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Methods

Three temporal inference approaches, each described in detail in docs/methods.md:

Method	Paper	CLIP MAE	Description
Time Probing	Sec. 3.1	9.24	Dot-product similarity baseline
UMAP Timeline	Sec. 3.3.1	13.01	1D manifold projection
Bezier(R^S, Int)	Sec. 3.3.2	8.80	Bezier curve in KPCA subspace (best)

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Supported Models (37 VLMs)

Family	Count	Backend
CLIP	9	openai/CLIP
EVA-CLIP	8	eva_clip (BAAI)
OpenCLIP	10	open_clip
SigLIP	3	open_clip
Others (CoCa, MobileCLIP, ViTamin, CLIPA, ImageBind, ViT-Lens)	7	various

See docs/models.md for the full list with model keys and installation instructions.

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Documentation

• docs/library.md — Full Python API reference
• docs/methods.md — Detailed method descriptions
• docs/models.md — All 37 VLMs documented
• docs/dataset.md — TIME10k dataset details
• docs/reproducing_results.md — Step-by-step reproduction guide

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Citation

@inproceedings{10.1145/3746027.3758163,
  author = {Tekaya, Nidham and Waldner, Manuela and Zeppelzauer, Matthias},
  title = {A Matter of Time: Revealing the Structure of Time in Vision-Language Models},
  year = {2025},
  isbn = {9798400720352},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3746027.3758163},
  doi = {10.1145/3746027.3758163},
  booktitle = {Proceedings of the 33rd ACM International Conference on Multimedia},
  pages = {12371--12380},
  numpages = {10},
  keywords = {benchmark dataset, multimodal representations, time estimation, time modeling, time reasoning, vision-language models},
  location = {Dublin, Ireland},
  series = {MM '25}
}

Links

• PyPI Package
• Paper (ACM Digital Library)
• arXiv Preprint
• Project Page
• TIME10k Dataset
• Hugging Face Demo

License

This project is licensed under the MIT License - see the LICENSE file for details.