# Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation

arXiv: https://arxiv.org/abs/1603.04871 · HTML: https://mlsanity.com/paper/1603.04871/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2016.

## Built on by (most cited first, 2 of 2)

- [Contrastive Proposal Extension with LSTM Network for Weakly Supervised Object Detection](https://mlsanity.com/paper/2110.07511/) (2021)
- [RethNet: Object-by-Object Learning for Detecting Facial Skin Problems](https://mlsanity.com/paper/2101.02127/) (2021)

## Abstract (arXiv)

State-of-the-art results of semantic segmentation are established by Fully Convolutional neural Networks (FCNs). FCNs rely on cascaded convolutional and pooling layers to gradually enlarge the receptive fields of neurons, resulting in an indirect way of modeling the distant contextual dependence. In this work, we advocate the use of spatially recurrent layers (i.e. ReNet layers) which directly capture global contexts and lead to improved feature representations. We demonstrate the effectiveness of ReNet layers by building a Naive deep ReNet (N-ReNet), which achieves competitive performance on Stanford Background dataset. Furthermore, we integrate ReNet layers with FCNs, and develop a novel Hybrid deep ReNet (H-ReNet). It enjoys a few remarkable properties, including full-image receptive fields, end-to-end training, and efficient network execution. On the PASCAL VOC 2012 benchmark, the H-ReNet improves the results of state-of-the-art approaches Piecewise, CRFasRNN and DeepParsing by 3.6%, 2.3% and 0.2%, respectively, and achieves the highest IoUs for 13 out of the 20 object classes.