IDRNet: Intervention-Driven Relation Network for Semantic Segmentation
Authors: Zhenchao Jin, Xiaowei Hu, Lingting Zhu, Luchuan Song, Li Yuan, Lequan Yu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted to validate the effectiveness of IDRNet quantitatively and qualitatively. |
| Researcher Affiliation | Collaboration | 1The University of Hong Kong 2Shanghai AI Laboratory 3Peking University 4University of Rochester |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Figure 2 is a diagram of the network. |
| Open Source Code | Yes | Code is available at https://github.com/SegmentationBLWX/sssegmentation. |
| Open Datasets | Yes | Our approach is validated on five popular semantic segmentation benchmark datasets, including ADE20K [87], COCO-Stuff [4], Cityscapes [17], LIP [29] and PASCAL-Context [22]. |
| Dataset Splits | Yes | In detail, ADE20K is one of the most well-known datasets for scene parsing, which contains 150 stuff/object category labels. There are 20K/2K/3K images for training, validation and test set, respectively in the dataset. COCO-Stuff...It consists of 9K/1K images in the training and test sets. Cityscapes has 5K high-resolution annotated urban scene images, with 2,975/500/1,524 images for training/validation/testing. LIP mainly focuses on single human parsing and contains 50K images with 19 semantic human part classes and 1 background class. Its training, validation and test sets separately involve 30K/10K/10K images. PASCAL-Context...The dataset is divided into 4,998 and 5,105 images for training and validation. |
| Hardware Specification | Yes | Table 5: Complexity comparison with existing context modules on a single RTX 3090 Ti GPU. |
| Software Dependencies | No | Our algorithm is implemented in Py Torch [54] and SSSegmentation [38]. No specific version numbers for PyTorch or SSSegmentation are provided. |
| Experiment Setup | Yes | Specific to ADE20K, we set learning rate, crop size, batch size and training epochs as 0.01, 512 × 512, 16 and 130, respectively. Specific to COCO-Stuff, learning rate, crop size, batch size and training epochs are set as 0.001, 512 × 512, 16 and 110, respectively. As for LIP, we set learning rate, crop size, batch size and training epochs as 0.01, 473 × 473, 32 and 150, respectively. As for Cityscapes, learning rate, crop size, batch size and training epochs are set as 0.01, 512 × 1024, 8 and 220, respectively. Specific to PASCAL-Context, we set learning rate, crop size, batch size and training epochs as 0.004, 480 × 480, 16 and 260, respectively. |