STDiff: Spatio-Temporal Diffusion for Continuous Stochastic Video Prediction
Authors: Xi Ye, Guillaume-Alexandre Bilodeau
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the performance of the proposed STDiff model on KITTI (Geiger et al. 2013), Cityscapes (Cordts et al. 2016), KTH (Schuldt, Laptev, and Caputo 2004), BAIR (Ebert et al. 2017) and stochastic moving MNIST (SMMNIST) (Denton and Fergus 2018) datasets. |
| Researcher Affiliation | Academia | LITIV, Polytechnique Montr eal xi.ye@polymtl.ca, gabilodeau@polymtl.ca |
| Pseudocode | Yes | Algorithm 1: Training |
| Open Source Code | Yes | Our code is available at https: //github.com/Xi Ye20/STDiff Project. |
| Open Datasets | Yes | We evaluated the performance of the proposed STDiff model on KITTI (Geiger et al. 2013), Cityscapes (Cordts et al. 2016), KTH (Schuldt, Laptev, and Caputo 2004), BAIR (Ebert et al. 2017) and stochastic moving MNIST (SMMNIST) (Denton and Fergus 2018) datasets. |
| Dataset Splits | No | The number of past frames and future frames to predict is determined according to the experimental protocols of previous works (see appendix in the ar Xiv version of this paper for more details (Ye and Bilodeau 2023a)). |
| Hardware Specification | Yes | All models are trained with 4 NVIDIA V100 Volta GPU (32G memory). |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | No | The paper describes some general training procedures like random sampling of future time steps and the number of predictions/sampling steps, but it does not provide specific hyperparameters such as learning rate, batch size, or optimizer settings in the main text. |