Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images
Authors: Yafei YANG, Bo Yang
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,844 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,292 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate current unsupervised approaches in a large-scale experimental study. We implement 4 representative methods and train more than 130 models on 6 curated datasets from scratch. |
| Researcher Affiliation | Academia | v LAR Group, The Hong Kong Polytechnic University EMAIL EMAIL |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm', nor are there any structured algorithm blocks presented. |
| Open Source Code | Yes | The datasets, code and pretrained models are available at https://github.com/vLAR-group/UnsupObjSeg |
| Open Datasets | Yes | three commonly-used synthetic datasets: d Sprites [42], Tetris [34] and CLEVR [33], 2) three real-world datasets: YCB [9], Scan Net [17], and COCO [38] |
| Dataset Splits | No | Each dataset has about 10000 images for training, 2000 images for testing. No explicit mention of a separate validation split is found. |
| Hardware Specification | No | The main body of the paper does not specify the exact hardware used for experiments (e.g., specific GPU models, CPU models, or cloud instances). While the checklist indicates this information is in the Appendix, the Appendix content is not provided in the given text. |
| Software Dependencies | No | The main body of the paper does not specify software dependencies with version numbers. While the ethics checklist points to 'Implementation Details in Appendix' for training details, the Appendix content is not provided in the given text. |
| Experiment Setup | No | The main body of the paper refers to 'Implementation details' and 'Preparation details for each dataset' being provided in the Appendix, where training details and hyperparameters are typically found. However, the Appendix content is not available in the provided text. |