# Stacked Deconvolutional Network for Semantic Segmentation

arXiv: https://arxiv.org/abs/1708.04943 · HTML: https://mlsanity.com/paper/1708.04943/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2017.

## Built on by (most cited first, 18 of 18)

- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Semantic Image Segmentation: Two Decades of Research](https://mlsanity.com/paper/2302.06378/) (2023)
- [CycleSegNet: Object Co-segmentation with Cycle Refinement and Region Correspondence](https://mlsanity.com/paper/2101.01308/) (2021)
- [PolyMaX: General Dense Prediction with Mask Transformer](https://mlsanity.com/paper/2311.05770/) (2023)
- [Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition](https://mlsanity.com/paper/2103.00259/) (2021)
- [Whole Brain Segmentation with Full Volume Neural Network](https://mlsanity.com/paper/2110.15601/) (2021)
- [Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving Applications](https://mlsanity.com/paper/2203.01087/) (2022)
- [Local Learning on Transformers via Feature Reconstruction](https://mlsanity.com/paper/2212.14215/) (2022)
- [Learning Class-Agnostic Pseudo Mask Generation for Box-Supervised Semantic Segmentation](https://mlsanity.com/paper/2103.05463/) (2021)
- [DeepSatData: Building large scale datasets of satellite images for training machine learning models](https://mlsanity.com/paper/2104.13824/) (2021)
- [Pixel-Level Domain Adaptation: A New Perspective for Enhancing Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2408.02039/) (2024)
- [Full-Resolution Encoder-Decoder Networks with Multi-Scale Feature Fusion for Human Pose Estimation](https://mlsanity.com/paper/2106.00566/) (2021)
- [BoundaryNet: An Attentive Deep Network with Fast Marching Distance Maps for Semi-automatic Layout Annotation](https://mlsanity.com/paper/2108.09433/) (2021)
- [PDFNet: Pointwise Dense Flow Network for Urban-Scene Segmentation](https://mlsanity.com/paper/2109.10083/) (2021)
- [L-MAE: Masked Autoencoders are Semantic Segmentation Datasets Augmenter](https://mlsanity.com/paper/2211.11242/) (2022)
- [Progressively Dual Prior Guided Few-shot Semantic Segmentation](https://mlsanity.com/paper/2211.15467/) (2022)
- [P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation](https://mlsanity.com/paper/2310.15025/) (2023)
- [Cross-Layer Attentive Feature Upsampling for Low-latency Semantic Segmentation](https://mlsanity.com/paper/2601.01167/) (2026)

## Abstract (arXiv)

Recent progress in semantic segmentation has been driven by improving the spatial resolution under Fully Convolutional Networks (FCNs). To address this problem, we propose a Stacked Deconvolutional Network (SDN) for semantic segmentation. In SDN, multiple shallow deconvolutional networks, which are called as SDN units, are stacked one by one to integrate contextual information and guarantee the fine recovery of localization information. Meanwhile, inter-unit and intra-unit connections are designed to assist network training and enhance feature fusion since the connections improve the flow of information and gradient propagation throughout the network. Besides, hierarchical supervision is applied during the upsampling process of each SDN unit, which guarantees the discrimination of feature representations and benefits the network optimization. We carry out comprehensive experiments and achieve the new state-of-the-art results on three datasets, including PASCAL VOC 2012, CamVid, GATECH. In particular, our best model without CRF post-processing achieves an intersection-over-union score of 86.6% in the test set.