# C-DLinkNet: considering multi-level semantic features for human parsing

Source: [C-DLinkNet: considering multi-level semantic features for human parsing](<https://www.sota2.com/research/paper/68fa67581e209131cea15b33>)

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

Data updated: 2025-10-28T13:56:42.488Z

Authors: Yu Lu, Muyan Feng, Ming Wu, Chuang Zhang

Published: 2020-01-31T07:47:45.000Z

## Abstract

Human parsing is an essential branch of semantic segmentation, which is a fine-grained semantic segmentation task to identify the constituent parts of human. The challenge of human parsing is to extract effective semantic features to resolve deformation and multi-scale variations. In this work, we proposed an end-to-end model called C-DLinkNet based on LinkNet, which contains a new module named Smooth Module to combine the multi-level features in Decoder part. C-DLinkNet is capable of producing competitive parsing performance compared with the state-of-the-art methods with smaller input sizes and no additional information, i.e., achiving mIoU=53.05 on the validation set of LIP dataset.

## 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/2001.11690>)

[PDF](<https://arxiv.org/pdf/2001.11690.pdf>)