Object-Centric Representation Learning with Generative Spatial-Temporal Factorization
Authors: Nanbo Li, Muhammad Ahmed Raza, Wenbin Hu, Zhaole Sun, Robert Fisher
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We used two simulated multi-view-dynamic-scene synthetic datasets, namely DRoom and MJCArm, and a real-world dataset, namely Cube Land (see Appendix C.3 for details), in this work. We conducted quantitative analysis on DRoom and show qualitative results on the other two datasets. |
| Researcher Affiliation | Academia | Li Nanbo School of Informatics University of Edinburgh nanbo.li@ed.ac.uk Muhammad Ahmed Raza School of Informatics University of Edinburgh m.a.raza@ed.ac.uk Hu Wenbin School of Informatics University of Edinburgh wenbin.hu@ed.ac.uk Zhaole Sun School of Informatics University of Edinburgh zhaole.sun@ed.ac.uk Robert B. Fisher School of Informatics University of Edinburgh rbf@inf.ed.ac.uk |
| Pseudocode | Yes | Algorithm 1: Dy MON Training Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability. |
| Open Datasets | Yes | We used two simulated multi-view-dynamic-scene synthetic datasets, namely DRoom and MJCArm, and a real-world dataset, namely Cube Land (see Appendix C.3 for details), in this work. |
| Dataset Splits | Yes | The DRoom dataset consists of five subsets (including both training and testing sets): one subset (denoted as DR0-|fz|) with zero object motion (multi-view-static-scene data), one subset (denoted as DR0-|fv|) with zero camera motion (single-view-dynamic-scene data), and three multi-view-dynamic-scene subsets of increasing speed difference levels from 1 to 3 (denoted as DR-Lvl.1 3). Each of the five subsets consists of around 200 training sequences (40 frames of RGB images per sequence) and 20 testing sequences (40 frames from 12 different views, i.e. 57.6k images). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | Hyperparameters |Q|, (βF CSO, βSCF O), ( t, τ) ; // t > τ > 2, |Q| = sizeof(Q) (Algorithm 1 Input) [...] All models were trained with 3 different random seeds for quantitative comparisons. Refer to our supplementary material for full details on experimental setups, and ablation studies and more qualitative results. |