| --- |
| language: |
| - en |
| tags: |
| - multimodal-learning |
| --- |
| # Dataset Card for TST-ProcTHOR |
|
|
| ## Dataset Description |
|
|
| - **Homepage:** [https://tst-vision.epfl.ch](https://tst-vision.epfl.ch) |
| - **Repository:** [TST official repository](https://github.com) |
| - **Paper:** [Arxiv](https://arxiv.org) |
|
|
| ### Dataset Summary |
|
|
| This custom TST-ProcTHOR dataset is used in research work "Multimodality as Supervision: Self-Supervised Specialization to the Test Environment via Multimodality". |
|
|
| - `pretrain/` is a multimodal pretraining dataset collected using ProcTHOR environment. It contains RGB images, and 9 additional tokenized modalities. |
|
|
| - `segmentation/train` is the associated downstream dataset used to finetune TST pretrained models on semantic segmentation tasks. |
|
|
| - `segmentation/test` contains the test dataset used for evaluation/testing on semantic segmentation task. This data corresponds to samples obtained from the test-space itself. |
|
|
| - `captioning/train` is the associated downstream dataset used to finetune TST pretrained models on captioning task. |
|
|
| - `captioning/test` contains the test dataset used for evaluation/testing on captioning task. This data corresponds to samples obtained from the test-space itself. |
|
|
| ## Dataset Structure |
|
|
| ```python |
| TST-ProcTHOR/ |
| ├── pretrain/ |
| │ ├── test_spaces/ |
| │ │ ├── crop_settings/ # Contains .tar shards |
| │ │ ├── det/ # Contains .tar shards |
| │ │ ├── rgb/ # Contains .tar shards |
| │ │ ├── tok_canny_edge@224/ # Contains .tar shards |
| │ │ ├── ... # More tokenized feature directories |
| │ │ └── tok_semseg@224/ # Contains .tar shards |
| │ └── transfer/ |
| │ ├── crop_settings/ # Contains .tar shards |
| │ ├── det/ # Contains .tar shards |
| │ ├── rgb/ # Contains .tar shards |
| │ ├── tok_canny_edge@224/ # Contains .tar shards |
| │ ├── ... # More tokenized feature directories |
| │ └── tok_semseg@224/ # Contains .tar shards |
| ├── segmentation/ |
| │ ├── train/ # Training data for segmentation |
| │ └── test/ # Test data for segmentation |
| ├── captioning/ |
| │ ├── train/ # Training data for captioning |
| │ └── test/ # Test data for captioning |
| └── README.md |
| ``` |
|
|
| ## Dataset Creation |
|
|
| It includes procedurally generated |
| house-like environments. We use 5 procedurally generated |
| houses as our test space. Dataset is collected by randomly sample various agent |
| x, y, z positions and orientations along its axis in the test |
| space, and collect RGB-D images at these points. |
|
|
| ### Source Data |
|
|
| Dataset is collected from ProcTHOR simulator. |
|
|
|
|
| ### Citation Information |
|
|
| ``` |
| @inproceedings{singh2026tst, |
| title={Multimodality as Supervision: Self-Supervised Specialization to the Test Environment via Multimodality}, |
| author={Kunal Pratap Singh and Ali Garjani and Rishubh Singh and Muhammad Uzair Khattak and Efe Tarhan and Jason Toskov and Andrei Atanov and O{\u{g}}uzhan Fatih Kar and Amir Zamir}, |
| booktitle={International Conference on Learning Representations (ICLR)}, |
| year={2026} |
| } |
| ``` |
