SSA-Seg: Semantic and Spatial Adaptive Pixel-level Classifier for Semantic Segmentation
Authors: Xiaowen Ma, Zhen-Liang Ni, Xinghao Chen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three publicly available benchmarks show that the proposed SSA-Seg significantly improves the segmentation performance of the baseline models with only a minimal increase in computational cost. |
| Researcher Affiliation | Collaboration | Xiaowen Ma1,2 , Zhenliang Ni1 , Xinghao Chen1 1Huawei Noah s Ark Lab 2Zhejiang University |
| Pseudocode | No | The paper describes its methods in text and figures but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | https://github.com/xwmaxwma/SSA-Seg. [...] We provide all the code and configuration files in order to reproduce the experiments in the paper. |
| Open Datasets | Yes | We perform experiments on the ADE20K [61], PASCAL-Context [39] and COCO-Stuff-10K [1] datasets. [...] The datasets are publicly available and can be downloaded. |
| Dataset Splits | Yes | For ADE20K and COCO-Stuff-10K, we have a cropping size of 512 512, while for PASCAL-Context, we have a cropping size of 480 480. In addition, the batch size of all datasets is 16, and the total iterations for ADE20K, COCO-Stuff-10K and PASCAL-Context number are 160k, 80k and 80k, respectively. [...] The training set, validation set, and the test set contain 20210, 2000, and 3352 images respectively. |
| Hardware Specification | Yes | The latency (ms) is calculated on the input size of 512 512 on V100 GPU. |
| Software Dependencies | Yes | We use MMSegmentation [12] and follow the common training settings. |
| Experiment Setup | Yes | During training, we apply data enhancement sequentially by random horizontal flipping, random resizing with a scale between 0.5 and 2.0, and random cropping. For ADE20K and COCO-Stuff-10K, we have a cropping size of 512 512, while for PASCAL-Context, we have a cropping size of 480 480. In addition, the batch size of all datasets is 16, and the total iterations for ADE20K, COCO-Stuff-10K and PASCAL-Context number are 160k, 80k and 80k, respectively. [...] In the implementation of this paper, λr, λp, λs are all set to 1, and the edge size of boundary is set to 4. |