A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://arxiv.org/ftp/arxiv/papers/2108/2108.03618.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
MPI: Multi-receptive and Parallel Integration for Salient Object Detection
[article]
<span title="2021-08-08">2021</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
Preserved Fulltext
The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantly improve the model's performance on salient object detection. In this paper, a novel method called MPI is proposed for salient object detection. Firstly, a multi-receptive enhancement module (MRE) is designed to effectively expand the receptive fields of features from different layers and generate features with
<span class="external-identifiers">
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.03618v1">arXiv:2108.03618v1</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ek7dosr4vnckxdz45qmpe5r4iq">fatcat:ek7dosr4vnckxdz45qmpe5r4iq</a>
</span>
more »
... ent receptive fields. MRE can enhance the semantic representation and improve the model's perception of the image context, which enables the model to locate the salient object accurately. Secondly, in order to reduce the reuse of redundant information in the complex top-down fusion method and weaken the differences between semantic features, a relatively simple but effective parallel fusion strategy (PFS) is proposed. It allows multi-scale features to better interact with each other, thus improving the overall performance of the model. Experimental results on multiple datasets demonstrate that the proposed method outperforms state-of-the-art methods under different evaluation metrics.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210812153414/https://arxiv.org/ftp/arxiv/papers/2108/2108.03618.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/8c/9f/8c9fe8fce7936acea501ddbe9b5c592e6504d1b4.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.03618v1" title="arxiv.org access">
<button class="ui compact blue labeled icon button serp-button">
<i class="file alternate outline icon"></i>
arxiv.org
</button>
</a>