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.
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
Authors: Mengcheng Lan, Xinjiang Wang, Yiping Ke, Jiaxing Xu, Litong Feng, Wayne Zhang
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 19,252 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,845 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 | Our Smoo Seg significantly outperforms STEGO in terms of pixel accuracy on three datasets: COCOStuff (+14.9%), Cityscapes (+13.0%), and Potsdam-3 (+5.7%). |
| Researcher Affiliation | Collaboration | 1 S-Lab, Nanyang Technological University 2 SCSE, Nanyang Technological University 3 Sense Time Research |
| Pseudocode | Yes | Algorithm 1 Smoo Seg: Py Torch-like Pseudocode |
| Open Source Code | Yes | https://github.com/mc-lan/Smoo Seg |
| Open Datasets | Yes | We test on three datasets. COCOStuff [35] is a scene-centric dataset... Cityscapes [36] is a collection of street scene images... Potsdam-3 [3] is a remote sensing dataset... |
| Dataset Splits | No | The paper states training and testing image counts for Potsdam-3, but does not explicitly provide details about a separate validation set split for any of the datasets. |
| Hardware Specification | Yes | Our experiments were conducted using Py Torch [37] on an RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch [37]' but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The exponential moving average (EMA) hyper-parameter is set to α = 0.998. The dimension of the embedding space is D = 64. The temperature is set to τ = 0.1. We use the Adam optimizer [38] with a learning rate of 1 10 4 and 5 10 4 for the projector and predictor, respectively. We set a batch size of 32 for all datasets. We train our model with a total of 3000 iterations for Cityscapes and Potsdam-3 datasets, and 8000 iterations for the COCOStuff dataset. |