Importance-Aware Semantic Segmentation for Autonomous Driving System
Authors: Bi-ke Chen, Chen Gong, Jian Yang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on Cam Vid and Cityscapes datasets reveal that by employing the proposed loss function, the existing deep learning models including FCN, Seg Net and ENet are able to consistently obtain the improved segmentation results on the pre-deļ¬ned important classes for safe-driving. |
| Researcher Affiliation | Academia | Bi-ke Chen, Chen Gong, Jian Yang School of Computer Science and Engineering, Nanjing University of Science and Technology Nanjing, 210094, China {bikechen, chen.gong, csjyang}@njust.edu.cn |
| Pseudocode | No | The paper provides mathematical derivations of its forward and backward propagation rules but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code, nor does it explicitly state that the code will be released or is available in supplementary materials. |
| Open Datasets | Yes | We use the Cam Vid dataset [Brostow et al., 2009] mentioned in Section 3 and a recent Cityscapes [Cordts et al., 2016] dataset for our experiments. |
| Dataset Splits | Yes | Cam Vid contains 367 training images, 26 validation images, and 233 test images. The resolution of images in this dataset is 960 720. Cityscapes is also a high-quality dataset for semantic scene understanding captured from the view of cockpit, which contains 2975 color training images, 500 validation images, and 1525 test images. |
| Hardware Specification | No | The paper mentions running experiments with deep neural networks but does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for these experiments. |
| Software Dependencies | No | The paper mentions using deep learning models like FCN, Seg Net, and ENet, but it does not specify any software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions) needed for replication. |
| Experiment Setup | No | The paper describes the proposed Importance-Aware Loss and its application, but it does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, number of epochs) or training configurations for the neural networks used. |