Renovating Names in Open-Vocabulary Segmentation Benchmarks
Authors: Haiwen Huang, Songyou Peng, Dan Zhang, Andreas Geiger
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments |
| Researcher Affiliation | Collaboration | 1 Bosch Io C Lab, University of T ubingen 3 T ubingen AI Center 3 Autonomous Vision Group, University of T ubingen 4 ETH Zurich 5 MPI for Intelligent Systems, T ubingen 6 Bosch Center for Artificial Intelligence |
| Pseudocode | No | The paper describes the method using text and figures (e.g., Fig. 2), but does not contain a formal pseudocode or algorithm block. |
| Open Source Code | Yes | We provide our code and relabelings for several popular segmentation datasets to the research community on our project page: https://andrehuang.github.io/renovate/ . |
| Open Datasets | Yes | We renovate three panoptic segmentation datasets respectively: MS COCO [13], ADE20K [14], and Cityscapes [15]. |
| Dataset Splits | Yes | We train a renaming model for 60k iterations with a batch size of 16 on the training set, then generate RENOVATE names for the entire dataset. |
| Hardware Specification | Yes | Training one renaming model on 4 A-100 GPUs requires approximately 3 days. |
| Software Dependencies | No | The paper mentions software like GPT-4, CLIP, Mask2Former, CaSED, and AdamW, but does not provide specific version numbers for these or other key software components used in the experiments. |
| Experiment Setup | Yes | We train a renaming model for 60k iterations with a batch size of 16 on the training set |