Enhancing Nighttime Semantic Segmentation with Visual-Linguistic Priors and Wavelet Transform
Authors: Jianhou Zhou, Xiaolong Zhou, Sixian Chan, Zhaomin Chen, Xiaoqin Zhang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmarks including Night City, Night Cityfine, BDD100K, and City Scapes demonstrate our method s superior performance over existing approaches. Our contributions are summarized as follows: Extensive experiments on various challenging benchmarks show that the proposed method outperforms stateof-the-art nighttime semantic segmentation. |
| Researcher Affiliation | Academia | Jianhou Zhou1 , Xiaolong Zhou2 , Sixian Chan3 and Zhaomin Chen4 , Xiaoqin Zhang3 1Hangzhou Dianzi University 2Quzhou University 3Zhejiang University Of Technology 4Wenzhou University naiive EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper includes figures illustrating the architecture and components (Figure 2, 3, 4) and mathematical equations, but it does not contain explicit pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Following previous practices[Deng et al., 2022; Wei et al., 2023; Cheng et al., 2022], we evaluate the nighttime semantic segmentation performance of our method on four datasets: Night City, Night City-fine, City Scapes, and BDD100K. Night City [Tan et al., 2021], the largest nighttime semantic segmentation dataset, contains 4,297 real nighttime images... City Scapes [Cordts et al., 2016], an autonomous driving dataset... BDD100K [Yu et al., 2020] is a large-scale driving dataset... |
| Dataset Splits | Yes | Night City [Tan et al., 2021], the largest nighttime semantic segmentation dataset, contains 4,297 real nighttime images, with 2,998 for training and 1,299 for validation. All images are 1024 512 in resolution... City Scapes [Cordts et al., 2016], an autonomous driving dataset, contains daytime images from 50 cities, with 2,975 training and 500 validation images, all at 2048 1024 resolution... We use a subset, BDD100K-night, for supplementary experiments, with 314 nighttime images for training and 31 for validation. |
| Hardware Specification | No | The paper mentions implementing the model using the MMSegmentation framework and using a batch size of 16, but it does not provide any specific details about the hardware (e.g., GPU models, CPU types) used for the experiments. |
| Software Dependencies | No | Our model is implemented using the MMSegmentation framework. The paper does not provide specific version numbers for MMSegmentation or any other software libraries or dependencies. |
| Experiment Setup | Yes | During training, we apply the recommended preprocessing methods from MMSegmentation, including mean normalization, random scaling, and flipping, on the Night City, Night City-fine, and City Scapes datasets. We use the default City Scapes 90k training configuration with a batch size of 16. For wavelet image reconstruction, λ = 0.1 and µ = 10, with their effects on segmentation accuracy tested in the ablation experiments. To handle size variations during inference, we use input image versions rescaled by factors of [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]. Additionally, we apply horizontal flipping and average the predictions from all augmented versions. |