# Dynamic Dual Sampling Module for Fine-Grained Semantic Segmentation

Source: [Dynamic Dual Sampling Module for Fine-Grained Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6a001e209131cea254a1>)

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

Data updated: 2025-10-25T23:13:37.567Z

Authors: Chen Shi, Xiangtai Li, Yanran Wu, Yunhai Tong, Yi Xu

Published: 2021-05-25T04:25:47.000Z

## Abstract

Representation of semantic context and local details is the essential issue for building modern semantic segmentation models. However, the interrelationship between semantic context and local details is not well explored in previous works. In this paper, we propose a Dynamic Dual Sampling Module (DDSM) to conduct dynamic affinity modeling and propagate semantic context to local details, which yields a more discriminative representation. Specifically, a dynamic sampling strategy is used to sparsely sample representative pixels and channels in the higher layer, forming adaptive compact support for each pixel and channel in the lower layer. The sampled features with high semantics are aggregated according to the affinities and then propagated to detailed lower-layer features, leading to a fine-grained segmentation result with well-preserved boundaries. Experiment results on both Cityscapes and Camvid datasets validate the effectiveness and efficiency of the proposed approach. Code and models will be available at \\url{x3https://github.com/Fantasticarl/DDSM}.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2105.11657>)

[PDF](<https://arxiv.org/pdf/2105.11657.pdf>)