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Methods › Computer Vision › Semantic Segmentation Models › CABiNet

Context Aggregated Bi-lateral Network for Semantic Segmentation

CABiNet

23 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

With the increasing demand of autonomous systems, pixelwise semantic segmentation for visual scene understanding needs to be not only accurate but also efficient for potential real-time applications. In this paper, we propose Context Aggregation Network, a dual branch convolutional neural network, with significantly lower computational costs as compared to the state-of-the-art, while maintaining a competitive prediction accuracy. Building upon the existing dual branch architectures for high-speed semantic segmentation, we design a high resolution branch for effective spatial detailing and a context branch with light-weight versions of global aggregation and local distribution blocks, potent to capture both long-range and local contextual dependencies required for accurate semantic segmentation, with low computational overheads. We evaluate our method on two semantic segmentation datasets, namely Cityscapes dataset and UAVid dataset. For Cityscapes test set, our model achieves state-of-the-art results with mIOU of 75.9%, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX. With regards to UAVid dataset, our proposed network achieves mIOU score of 63.5% with high execution speed (15 FPS).

Papers archive 2025-07-28

23 shown of 23, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 35 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Imitation Learning4
Navigate2
Action Segmentation1
Argument Mining1
Breast Cancer Detection1
Clustering1
GPU1
Image Registration1
In-Context Learning1
Large Language Model1
Management1
Motion Planning1
Object1
Object Detection1
Object Rearrangement1
Point Tracking1
Position1
Question Answering1
Reinforcement Learning1
Reinforcement Learning (RL)1

Usage over time archive 2025-07-28

Papers per year tagged with CABiNet: 2017 to 2025, peak 7 7 0 2017: 2 papers 2017 2018: 1 paper 2018 2019: 2 papers 2019 2020: 2 papers 2020 2021: 0 papers 2021 2022: 5 papers 2022 2023: 3 papers 2023 2024: 7 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (23 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Semantic Segmentation Models

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