Learning Invariant Inter-pixel Correlations for Superpixel Generation
Authors: Sen Xu, Shikui Wei, Tao Ruan, Lixin Liao
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on four benchmark datasets demonstrate the superiority of our approach to existing state-of-theart methods, regarding boundary adherence, generalization, and efficiency. |
| Researcher Affiliation | Collaboration | Sen Xu1,2, Shikui Wei*1,2, Tao Ruan3, Lixin Liao4 1Institute of Information Science, Beijing Jiaotong University 2Beijing Key Laboratory of Advanced Information Science and Network Technology 3Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University 4 Dao AI Robotics Inc. |
| Pseudocode | Yes | Algorithm 1: Training pseudocode for CDS. |
| Open Source Code | Yes | Code and pre-trained model are available at https://github.com/rookiie/CDSpixel. |
| Open Datasets | Yes | We evaluate our method on four segmentation datasets from different domains: BSDS500 (Arbelaez et al. 2010), NYUv2 (Silberman et al. 2012), KITTI (Geiger, Lenz, and Urtasun 2012), Pascal VOC2012 (Everingham et al. 2015). |
| Dataset Splits | No | The paper states: "we only train our model on the BSDS500 dataset and run inference on the other datasets." It mentions a "reduced validated set used by (Gadde et al. 2016) of Pascal VOC2012" in the context of a downstream task, but this is not a general validation split for their own model's training on BSDS500, nor are specific train/validation/test splits provided for BSDS500. |
| Hardware Specification | Yes | We use the gradient map as the auxiliary modality and conduct all the experiments on single RTX3090 GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | During the training phase, we apply data augmentation through random resize, random cropping to 208 208, and random horizontal/vertical flipping for our CDS. We trained the models using Adam optimizer. The learning rate starts at 5e-4 and is updated by the poly learning rate policy. We trained our model for 150k iterations with batch size eight, and superpixel grid size d is set to 16. |