# Supplementary Material for ICLR 2026 Submission #3595

## Concepts in Motion: Temporal Bottlenecks for Interpretable Video Classification

This repository contains supplementary material for our ICLR 2026 submission. All experiments were conducted using **Python 3.13.5**. To ensure reproducibility, please create a new environment and install the required packages as specified in the provided requirements file. The provided checkpoint is saved with cuda enabled, so make sure that you also run your experiments with a machine having GPUs.

**Note:** Due to the 100MB submission limit, only essential deliverables are included. Please refer to the appendix for direct links to all mentioned files.

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## Key Scripts

- [`save_videos.py`](./save_videos.py): Generates video and image data for the covered datasets.
- [`embedding.py`](./embedding.py): Creates all necessary embeddings for MoTIF.
- [`train_MoTIF.py`](./train_MoTIF.py): Trains your model and defines hyperparameters. Ensure your video data is in the correct folder, or that videos are already properly embedded.
- [`MoTIF.ipynb`](./MoTIF.ipynb): Interactive notebook for visualizing results. Download checkpoints and place them in the `Models` folder. A text file with descriptions is also included.
- [`MoTIF-ST.ipynb`](./MoTIF.ipynb): Interactive notebook for visualizing results with space-time transformer (new in rebuttal).

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## Folder Structure

- **Datasets/**: Placeholder for datasets.
- **Embeddings/**: Placeholder for embeddings.
- **Models/**: Placeholder for trained model checkpoints.
- **utils/**: Relevant utility code.
- **Videos/**: Videos and explanations used in the paper.

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## Reproducibility Instructions

1. **Create a new Python 3.13.5 environment.**
2. **Install dependencies** from the requirements file:`pip install -r requirements.txt`
3. **Download and place datasets** in the `Datasets` folder.
4. **Run the provided scripts** to generate embeddings and train models.
5. **Visualize results and checkpoints** using [`MoTIF.ipynb`](./MoTIF.ipynb).

For further details, please refer to the documentation in each script. If you encounter any issues, contact us or check the appendix for clarifications.

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## Appendix: File Links

- [`save_videos.py`](./save_videos.py)
- [`embedding.py`](./embedding.py)
- [`train_MoTIF.py`](./train_MoTIF.py)
- [`MoTIF.ipynb`](./MoTIF.ipynb)
- [`requirements.txt`](./requirements.txt)

### Additional Information

- The `utils/core` directory contains code from the Perception Encoder framework, which has been used as-is without modifications.
- For rebuttal, we added:
  - `motif_spacetime_full.py` without diagonal attention
  - `motif_spacetime.py` with diagonal attention

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Thank you for your interest in our work!

Best

Authors of MoTIF
