Transmission-Guided Bayesian Generative Model for Smoke Segmentation
Authors: Siyuan Yan, Jing Zhang, Nick Barnes3009-3017
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on benchmark testing datasets illustrate that our model achieves both accurate predictions and reliable uncertainty maps representing model ignorance about its prediction. |
| Researcher Affiliation | Academia | The Australian National University {siyuan.yan, jing.zhang, nick.barnes}@anu.edu.au |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. The method is described in text and through network architecture diagrams. |
| Open Source Code | Yes | Our code and dataset are publicly available at: https://github.com/redlessme/Transmission-BVM. |
| Open Datasets | Yes | Our code and dataset are publicly available at: https://github.com/redlessme/Transmission-BVM. ... To promote the development of this field, we provide the first high-quality, large-scale smoke dataset with 5,000 training images and 400 testing images, where the training set consists of 1,400 real life images and 4,000 synthetic images with pixel-wise annotation, and the test images are all real life images. |
| Dataset Splits | No | The paper specifies training and testing sets (5000 training images, 400 testing images) and discusses evaluation on these. However, it does not explicitly define a separate validation set split or how validation was performed to tune hyperparameters during training. Phrases like 'training dataset' and 'testing images' are used, but a distinct 'validation' split is not specified. |
| Hardware Specification | Yes | It took about three days of training on SYN70K and 15 hours on SMOKE5K with batch size 6 using a single NVIDIA Ge Force RTX 2080Ti GPU. |
| Software Dependencies | No | The paper mentions using "Py Torch" but does not specify a version number or other key software components with their versions (e.g., Python version, CUDA version, specific libraries used with versions). |
| Experiment Setup | Yes | Each image is re-scaled to 480 480. Empirically, we set the dimension of the latent space z as 8. The learning rates of the generator Pθ(y|x, z) and the uncertainty estimation network gγ are initialized to 2.5e-5 and 1.5e-5, respectively. We use the Adam optimizer and decrease the learning rate 0.8 after 40 epochs with a maximum epoch of 50. We adopt Res Net50 backbone to the encoder of Pθ(y|x, z), which is initialized with parameters trained for image classification, and the other newly added layers are initialized with the Py Torch default initialization strategy. |