# Capsules for Object Segmentation

arXiv: https://arxiv.org/abs/1804.04241 · HTML: https://mlsanity.com/paper/1804.04241/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2018.

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

- [Salient Object Detection via Integrity Learning](https://mlsanity.com/paper/2101.07663/) (2021)
- [Efficient-CapsNet: Capsule Network with Self-Attention Routing](https://mlsanity.com/paper/2101.12491/) (2021)
- [Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules](https://mlsanity.com/paper/2105.04836/) (2021)
- [SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data](https://mlsanity.com/paper/2201.05905/) (2022)
- [Learning with Capsules: A Survey](https://mlsanity.com/paper/2206.02664/) (2022)
- [Weakly Supervised Grounding for VQA in Vision-Language Transformers](https://mlsanity.com/paper/2207.02334/) (2022)
- [3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation](https://mlsanity.com/paper/2203.08965/) (2022)
- [DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation](https://mlsanity.com/paper/2112.13559/) (2021)
- [Conditional diffusion model with spatial attention and latent embedding for medical image segmentation](https://mlsanity.com/paper/2502.06997/) (2025)
- [Deformable Capsules for Object Detection](https://mlsanity.com/paper/2104.05031/) (2021)
- [A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets](https://mlsanity.com/paper/2107.07927/) (2021)
- [3DConvCaps: 3DUnet with Convolutional Capsule Encoder for Medical Image Segmentation](https://mlsanity.com/paper/2205.09299/) (2022)
- [Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning](https://mlsanity.com/paper/2107.04983/) (2021)
- [Federated Learning for Large Models in Medical Imaging: A Comprehensive Review](https://mlsanity.com/paper/2508.20414/) (2025)
- [Hybrid Gromov-Wasserstein Embedding for Capsule Learning](https://mlsanity.com/paper/2209.00232/) (2022)
- [Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans](https://mlsanity.com/paper/2104.10903/) (2021)
- [Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions](https://mlsanity.com/paper/2107.11517/) (2021)
- [Parallel Capsule Networks for Classification of White Blood Cells](https://mlsanity.com/paper/2108.02644/) (2021)
- [Capsule networks with non-iterative cluster routing](https://mlsanity.com/paper/2109.09213/) (2021)
- [CapsNet for Medical Image Segmentation](https://mlsanity.com/paper/2203.08948/) (2022)
- [A Novel Capsule Neural Network Based Model for Drowsiness Detection Using Electroencephalography Signals](https://mlsanity.com/paper/2204.01666/) (2022)
- [Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation Learning](https://mlsanity.com/paper/2206.13465/) (2022)
- [Single Image Super-Resolution Based on Capsule Neural Networks](https://mlsanity.com/paper/2210.03743/) (2022)
- [CapsFlow: Optical Flow Estimation with Capsule Networks](https://mlsanity.com/paper/2304.00306/) (2023)
- [Capsule network with shortcut routing](https://mlsanity.com/paper/2307.10212/) (2023)
- [1D-CapsNet-LSTM: A Deep Learning-Based Model for Multi-Step Stock Index Forecasting](https://mlsanity.com/paper/2310.02090/) (2023)
- [DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object Segmentation](https://mlsanity.com/paper/2311.00483/) (2023)
- [3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation](https://mlsanity.com/paper/2403.16350/) (2024)
- [Mitigating Vanishing Activations in Deep CapsNets Using Channel Pruning](https://mlsanity.com/paper/2410.16908/) (2024)
- [The Convergence of Dynamic Routing between Capsules](https://mlsanity.com/paper/2501.06240/) (2025)

## Abstract (arXiv)

Convolutional neural networks (CNNs) have shown remarkable results over the last several years for a wide range of computer vision tasks. A new architecture recently introduced by Sabour et al., referred to as a capsule networks with dynamic routing, has shown great initial results for digit recognition and small image classification. The success of capsule networks lies in their ability to preserve more information about the input by replacing max-pooling layers with convolutional strides and dynamic routing, allowing for preservation of part-whole relationships in the data. This preservation of the input is demonstrated by reconstructing the input from the output capsule vectors. Our work expands the use of capsule networks to the task of object segmentation for the first time in the literature. We extend the idea of convolutional capsules with locally-connected routing and propose the concept of deconvolutional capsules. Further, we extend the masked reconstruction to reconstruct the positive input class. The proposed convolutional-deconvolutional capsule network, called SegCaps, shows strong results for the task of object segmentation with substantial decrease in parameter space. As an example application, we applied the proposed SegCaps to segment pathological lungs from low dose CT scans and compared its accuracy and efficiency with other U-Net-based architectures. SegCaps is able to handle large image sizes (512 x 512) as opposed to baseline capsules (typically less than 32 x 32). The proposed SegCaps reduced the number of parameters of U-Net architecture by 95.4% while still providing a better segmentation accuracy.