# LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation

arXiv: https://arxiv.org/abs/1707.03718 · HTML: https://mlsanity.com/paper/1707.03718/

## Key facts

- Cited by 214 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2017.

## Built on by (most cited first, 30 of 214)

- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)
- [Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization](https://mlsanity.com/paper/2107.04649/) (2021)
- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding](https://mlsanity.com/paper/2211.15660/) (2022)
- [A Billion-scale Foundation Model for Remote Sensing Images](https://mlsanity.com/paper/2304.05215/) (2023)
- [SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge](https://mlsanity.com/paper/2401.00496/) (2023)
- [EndoMapper dataset of complete calibrated endoscopy procedures](https://mlsanity.com/paper/2204.14240/) (2022)
- [Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation](https://mlsanity.com/paper/2304.12637/) (2023)
- [Capturing Omni-Range Context for Omnidirectional Segmentation](https://mlsanity.com/paper/2103.05687/) (2021)
- [AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation](https://mlsanity.com/paper/2308.03726/) (2023)
- [Using DUCK-Net for Polyp Image Segmentation](https://mlsanity.com/paper/2311.02239/) (2023)
- [Semantic Image Segmentation: Two Decades of Research](https://mlsanity.com/paper/2302.06378/) (2023)
- [Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection](https://mlsanity.com/paper/2206.00515/) (2022)
- [Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark](https://mlsanity.com/paper/2202.07054/) (2022)
- [AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation](https://mlsanity.com/paper/2306.06842/) (2023)
- [The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation](https://mlsanity.com/paper/2108.06800/) (2021)
- [FUSeg: The Foot Ulcer Segmentation Challenge](https://mlsanity.com/paper/2201.00414/) (2022)
- [ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgery](https://mlsanity.com/paper/2112.08189/) (2021)
- [Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2402.07245/) (2024)
- [A Fine-Grained Dataset and its Efficient Semantic Segmentation for Unstructured Driving Scenarios](https://mlsanity.com/paper/2103.13109/) (2021)
- [A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation](https://mlsanity.com/paper/2106.07557/) (2021)
- [SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving](https://mlsanity.com/paper/2109.07701/) (2021)
- [DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing](https://mlsanity.com/paper/2201.01047/) (2022)
- [Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey](https://mlsanity.com/paper/2206.08304/) (2022)
- [Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image Segmentation](https://mlsanity.com/paper/2307.14052/) (2023)
- [Estimating Canopy Height at Scale](https://mlsanity.com/paper/2406.01076/) (2024)
- [BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification](https://mlsanity.com/paper/2506.14318/) (2025)
- [High-Definition Map Generation Technologies For Autonomous Driving](https://mlsanity.com/paper/2206.05400/) (2022)
- [Computation-efficient Deep Learning for Computer Vision: A Survey](https://mlsanity.com/paper/2308.13998/) (2023)
- [FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation](https://mlsanity.com/paper/2305.02961/) (2023)

## Abstract (arXiv)

Pixel-wise semantic segmentation for visual scene understanding not only needs to be accurate, but also efficient in order to find any use in real-time application. Existing algorithms even though are accurate but they do not focus on utilizing the parameters of neural network efficiently. As a result they are huge in terms of parameters and number of operations; hence slow too. In this paper, we propose a novel deep neural network architecture which allows it to learn without any significant increase in number of parameters. Our network uses only 11.5 million parameters and 21.2 GFLOPs for processing an image of resolution 3x640x360. It gives state-of-the-art performance on CamVid and comparable results on Cityscapes dataset. We also compare our networks processing time on NVIDIA GPU and embedded system device with existing state-of-the-art architectures for different image resolutions.