Due to the dataset size, we had to only include the code part and the checkpoint. The dataset is not included in this repository. Please find the dataset in:
Onken, Arno et al. (2017). Data from: Using matrix and tensor factorizations for the single-trial analysis of population spike trains [Dataset]. Dryad. https://doi.org/10.5061/dryad.4ch10
# Install the required packages pip3 install torch torchvision torchaudio lightning torchmetrics zarr python -m pip install tslearn # run train loop example PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 \ PYTHONPATH=. python src/trainer/Trainer.py -m \ task_name=eccv model.featrue_key=dinov2_feats_0 \ model=eccv dataset.cross_val_movie=True dataset.movie_name=movie01,movie03 tags='["eccv"]' # the predictions will be saved in the following directory # Mov1 -> Mov2 checkpoints/0/checkpoints # Mov2 -> Mov1 checkpoints/1/checkpoints
@misc{Wu2024ViST, title={Aligning Neuronal Coding of Dynamic Visual Scenes with Foundation Vision Models}, author={Rining Wu and Feixiang Zhou and Ziwei Yin and Jian K. Liu}, year={2024}, eprint={2407.10737}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.10737}, }
We thank Shanshan Jia, Zhile Yang, Zerui Yang and Jing Peng for the highly valuable discussions.