# Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Source: [Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6a711e209131cea28211>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:24:37.002Z

Authors: Bowen Zhang, Yifan Liu, Zhi Tian, Chunhua Shen

Published: 2021-07-30T04:50:56.000Z

## Abstract

Semantic segmentation requires per-pixel prediction for a given image. Typically, the output resolution of a segmentation network is severely reduced due to the downsampling operations in the CNN backbone. Most previous methods employ upsampling decoders to recover the spatial resolution. Various decoders were designed in the literature. Here, we propose a novel decoder, termed dynamic neural representational decoder (NRD), which is simple yet significantly more efficient. As each location on the encoder's output corresponds to a local patch of the semantic labels, in this work, we represent these local patches of labels with compact neural networks. This neural representation enables our decoder to leverage the smoothness prior in the semantic label space, and thus makes our decoder more efficient. Furthermore, these neural representations are dynamically generated and conditioned on the outputs of the encoder networks. The desired semantic labels can be efficiently decoded from the neural representations, resulting in high-resolution semantic segmentation predictions. We empirically show that our proposed decoder can outperform the decoder in DeeplabV3+ with only 30% computational complexity, and achieve competitive performance with the methods using dilated encoders with only 15% computation. Experiments on the Cityscapes, ADE20K, and PASCAL Context datasets demonstrate the effectiveness and efficiency of our proposed method.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on PASCAL Context (val)](<https://www.sota2.com/research/sota/semantic-segmentation-on-pascal-context-val.md>): Semantic segmentation; PASCAL Context (val); mIoU: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2107.14428>)

[PDF](<https://arxiv.org/pdf/2107.14428.pdf>)