x-jepa

X-JEPA Model Checkpoints

Pretrained checkpoints for the paper:

Latent Prediction Needs Alignment: A Controlled Study of Joint-Embedding Predictive Vision-Language Learning Mohammad Kohankhaki, Daniel Kusuma, Shirin Salehi, Carsten Kamp, Sigrid Brell-Cokcan, and Anke Schmeink — to appear at AACL-IJCNLP 2026

Code: github.com/mohkoh19/x-jepa

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Checkpoints

Weights are stored as safetensors, so they load without executing arbitrary code (no pickle).

Download

git clone https://github.com/mohkoh19/x-jepa.git
cd x-jepa
bash scripts/download_checkpoints.sh        # -> checkpoints/*.safetensors

or a single file:

wget https://huggingface.co/mohkoh/x-jepa/resolve/main/xjepa_pa_lam01.safetensors

Evaluation

The code repository resolves each file's architecture from configs/checkpoints/<name>.yaml, so a downloaded checkpoint can be evaluated directly:

python src/eval.py experiment=eval/coco_karpathy_zeroshot \
  ckpt_path=checkpoints/xjepa_pa_lam01.safetensors \
  paths.data_dir=/path/to/data

scripts/quantitative_eval.py runs the full main-results suite.

Main Results (Paper Table 1)

All values are percentages; VSR is AUROC.

Citation

@inproceedings{kohankhaki2026latent,
  title     = {Latent Prediction Needs Alignment: A Controlled Study of Joint-Embedding Predictive Vision-Language Learning},
  author    = {Kohankhaki, Mohammad and Kusuma, Daniel and Salehi, Shirin and Kamp, Carsten and Brell-Cokcan, Sigrid and Schmeink, Anke},
  booktitle = {Proceedings of AACL-IJCNLP 2026},
  year      = {2026},
  publisher = {Association for Computational Linguistics},
  note      = {To appear},
}

License

Released under CC BY-NC 4.0 (research and other non-commercial use, with attribution). Parts of the training code derive from the I-JEPA release, which is distributed under the same licence.