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Datasets:
EPFL-VILAB
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TST-Replica

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Tags:
multimodal-learning
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The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Dataset Card for TST-Replica

Dataset Summary

This custom TST-Replica 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 Replica simulation 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.

Dataset Structure Pretraining Data 

TST-Replica/
├── 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
└── README.md

Dataset Creation

We use Omnidata, to densely sample Replica meshes corresponding to the 5 scenes to build our pre-training dataset. We defer the details of the sampling procedure to Omnidata.

Source Data

Original dataset samples are collected from Omnidata framework.

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}
        }
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Homepage:
tst-vision.epfl.ch
Repository:
TST official repository
Paper:
Arxiv
Total file size:
33.9 GB

Collection including EPFL-VILAB/TST-Replica