ClusterFuG: Clustering Fully connected Graphs by Multicut
Authors: Ahmed Abbas, Paul Swoboda
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evidence on instance segmentation on Cityscapes and clustering of Image Net datasets shows the merits of our approach. |
| Researcher Affiliation | Academia | 1MPI for Informatics, Saarland Informatics Campus, Germany 2University of Mannheim, Germany. |
| Pseudocode | Yes | Algorithm 1: GAEC (Keuper et al., 2015) (...) Algorithm 2: Dense GAEC (...) Algorithm 3: Incremental NN update |
| Open Source Code | Yes | 1Code available at https://github.com/aabbas90/ cluster-fug. |
| Open Datasets | Yes | We evaluate clustering of the Image Net (Deng et al., 2009) validation set containing 50k images. (...) We evaluate our method on the task of panoptic segmentation (Kirillov et al., 2019) on the Cityscapes dataset (Cordts et al., 2016). |
| Dataset Splits | Yes | We evaluate clustering of the Image Net (Deng et al., 2009) validation set containing 50k images. (...) from 500 images of the Cityscapes validation set. |
| Hardware Specification | Yes | We evaluate on NVIDIA A40 GPU with 48GB of memory. (...) All CPU algorithms are run on an AMD 7502P CPU with a maximum of 16 threads to allow for faster nearest neighbour search. |
| Software Dependencies | No | The paper mentions software like 'scikit-learn' and 'Mo Cov3' (Malkov & Yashunin, 2018; Johnson et al., 2019) but does not provide specific version numbers for any of its software dependencies. |
| Experiment Setup | Yes | For all multicut algorithms on all datasets we set the value of affinity strength αi in (11) to 0.4, preferring small clusters. |