CoSW: Conditional Sample Weighting for Smoke Segmentation with Label Noise
Authors: Lujian Yao, Haitao Zhao, Zhongze Wang, Kaijie Zhao, Jingchao Peng
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments show that our approach achieves SOTA performance on both real-world and synthetic noisy smoke segmentation datasets. |
| Researcher Affiliation | Academia | Lujian Yao Haitao Zhao Zhongze Wang Kaijie Zhao Jingchao Peng School of Information Science and Engineering East China University of Science and Technology Shanghai, China {lujianyao,zzwang,kjzhao,pjc}@mail.ecust.edu.cn, haitaozhao@ecust.edu.cn |
| Pseudocode | No | The paper provides mathematical derivations and outlines a process flow in Figure 2, but it does not include a dedicated pseudocode block or algorithm listing. |
| Open Source Code | No | The paper has not yet been open-sourced for data and code. |
| Open Datasets | Yes | Due to the visual diversity and blurry edges of smoke, label noise in the large-scale real-world smoke datasets (Smoke Seg [53] and SMOKE5K [51]) is ubiquitous. Hence, we conduct experiments on both datasets as real-world noise evaluation. |
| Dataset Splits | Yes | We select 1,000 images from Smoke Seg and carefully re-annotate them to obtain clean labels. Among them, 700 images are used for training, and 300 images are used for validation. |
| Hardware Specification | No | The paper does not provide sufficient information on the computer resources, such as the type of comput workers, memory, and time of execution needed to reproduce the experiments. |
| Software Dependencies | No | We implement our method on MMSegmentation. Standard color jittering, random cropping, and random flipping are adopted for data augmentation during the training stage. |
| Experiment Setup | Yes | We utilize the Adam W optimizer, with learning rate starting at 6e-5 and scheduled according to the polynomial annealing policy. For the Smoke Seg, we crop images to a size of 512 512 for training, while for SMOKE5K, we follow the previous methods and resize the images to 480 480. |