Unsupervised Learning of Compositional Scene Representations from Multiple Unspecified Viewpoints
Authors: Jinyang Yuan, Bin Li, Xiangyang Xue8971-8979
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several specifically designed synthetic datasets have shown that the proposed method is able to effectively learn from multiple unspecified viewpoints. |
| Researcher Affiliation | Academia | Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University {yuanjinyang, libin, xyxue}@fudan.edu.cn |
| Pseudocode | Yes | Algorithm 1: Inference of Latent Variables |
| Open Source Code | Yes | 1Code is available at https://git.io/JDnne. |
| Open Datasets | Yes | Datasets: The experiments are performed on four multiviewpoint variants (referred to as CLEVR-M1 to CLEVRM4) of the commonly used CLEVR dataset that differ in the ranges to sample viewpoints and in the attributes of objects. Further details are described in the Supplementary Material. ...constructed based on the d Sprites (Matthey et al. 2017), Abstract Scene (Zitnick and Parikh 2013), and CLEVR (Johnson et al. 2017) datasets, in a way similar to the Multi-Objects Datasets (Kabra et al. 2019) but provides extra annotations (for evaluation only) of complete shapes of objects. |
| Dataset Splits | No | The paper states that 'All the methods are trained and tested with M = 4 and K = 7' and 'All the methods are trained and tested with K =6, K =5, and K =7 on the d Sprites, Abstract, and CLEVR datasets, respectively.' However, it does not provide specific percentages or counts for training, validation, and test splits, nor does it refer to predefined splits with citations that contain this information. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | While the paper mentions hyperparameters such as λ and α, it does not provide concrete values for these or other common experimental setup details like learning rate, batch size, number of epochs, or optimizer settings in the main text. |