{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T01:00:43Z","timestamp":1791248443288,"version":"4.1.0"},"reference-count":67,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,5]],"date-time":"2021-12-05T00:00:00Z","timestamp":1638662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007835","name":"Silesian University of Technology","doi-asserted-by":"publisher","award":["09\/020\/RGJ21\/0007."],"award-info":[{"award-number":["09\/020\/RGJ21\/0007."]}],"id":[{"id":"10.13039\/501100007835","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, deep learning-based methods, especially utilizing fully convolutional neural networks, have shown extraordinary performance in salient object detection. Despite its success, the clean boundary detection of the saliency objects is still a challenging task. Most of the contemporary methods focus on exclusive edge detection modules in order to avoid noisy boundaries. In this work, we propose leveraging on the extraction of finer semantic features from multiple encoding layers and attentively re-utilize it in the generation of the final segmentation result. The proposed Revise-Net model is divided into three parts: (a) the prediction module, (b) a residual enhancement module, and (c) reverse attention modules. Firstly, we generate the coarse saliency map through the prediction modules, which are fine-tuned in the enhancement module. Finally, multiple reverse attention modules at varying scales are cascaded between the two networks to guide the prediction module by employing the intermediate segmentation maps generated at each downsampling level of the REM. Our method efficiently classifies the boundary pixels using a combination of binary cross-entropy, similarity index, and intersection over union losses at the pixel, patch, and map levels, thereby effectively segmenting the saliency objects in an image. In comparison with several state-of-the-art frameworks, our proposed Revise-Net model outperforms them with a significant margin on three publicly available datasets, DUTS-TE, ECSSD, and HKU-IS, both on regional and boundary estimation measures.<\/jats:p>","DOI":"10.3390\/rs13234941","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T03:10:38Z","timestamp":1638760238000},"page":"4941","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Revise-Net: Exploiting Reverse Attention Mechanism for Salient Object Detection"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5304-4897","authenticated-orcid":false,"given":"Rukhshanda","family":"Hussain","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Jadavpur University, Kolkata 700032, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2574-8157","authenticated-orcid":false,"given":"Yash","family":"Karbhari","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Pune Vidyarthi Griha\u2019s College of Engineering and Technology, Pune 411009, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5206-272X","authenticated-orcid":false,"given":"Muhammad Fazal","family":"Ijaz","sequence":"additional","affiliation":[{"name":"Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9073-5347","authenticated-orcid":false,"given":"Marcin","family":"Wo\u017aniak","sequence":"additional","affiliation":[{"name":"Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9598-7981","authenticated-orcid":false,"given":"Pawan Kumar","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Jadavpur University, Kolkata 700106, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8813-4086","authenticated-orcid":false,"given":"Ram","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/0010-0285(80)90005-5","article-title":"A feature-integration theory of attention","volume":"12","author":"Treisman","year":"1980","journal-title":"Cogn. 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