A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment
Authors: Hanchen Xie, Jiageng Zhu, Mahyar Khayatkhoei, Jiazhi Li, Mohamed E. Hussein, Wael Abdalmageed
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using RPCIN as a probe, experiments conducted on the combinations of the proposed datasets reveal potential weaknesses of the vision-based long-term dynamics prediction model. |
| Researcher Affiliation | Academia | 1USC Information Sciences Institute, Marina del Rey, USA 2USC Thomas Lord Department of Computer Science, Los Angeles USA 3USC Ming Hsieh Department of Electrical and Computer Engineering, Los Angeles, USA 4Alexandria University, Alexandria, Egypt. |
| Pseudocode | No | The paper describes mathematical formulations and processes (e.g., Equation (1) for RPCIN) but does not present structured pseudocode or algorithm blocks (e.g., labeled 'Algorithm' or 'Pseudocode') for its own proposed contributions. |
| Open Source Code | Yes | Project is available at: https://github.com/vimal-isi-edu/VDP-EMC |
| Open Datasets | Yes | To this end, we propose Sim B-Border as an extension of Sim B by increasing the image size from 64 64 to 192 96, which includes more space for adding various contexts of environment, and introducing borders to the image boundaries... Thus, we propose four datasets: Sim B-Border, Sim B-Split, Blen B-Border, and Blen B-Split... Project is available at: https://github.com/vimal-isi-edu/VDP-EMC |
| Dataset Splits | Yes | 1000 videos with 100 frames in each video are generated for train and test individually. |
| Hardware Specification | Yes | Our experiments are conducted on two NVIDIA 1080ti GPUs. |
| Software Dependencies | No | The paper mentions software components and libraries like 'Adam optimizer', 'Hourglass', 'BN, IN, GN, LN', and 'k-means function of Open CV', along with relevant citations. However, it does not provide specific version numbers for these software packages or the programming language (e.g., Python, PyTorch) used. |
| Experiment Setup | Yes | We set total batch size to 40 and models are trained over 50K iterations. We used Adam optimizer (Kingma & Ba, 2015) and set learning rate to 2 10 4 with cosine learning rate decay (Loshchilov & Hutter, 2017). Weight decay is set to 1 10 6. |