{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-bi-directional-message-passing-model-for","title":"A Bi-Directional Message Passing Model for Salient Object Detection","arxiv_id":null,"date":"2018-06-01","proceeding":"CVPR 2018 6","authors":["Lu Zhang","Ju Dai","Huchuan Lu","You He","Gang Wang"],"abstract":"Recent progress on salient object detection is beneficial from Fully Convolutional Neural Network (FCN). The saliency cues contained in multi-level convolutional features are complementary for detecting salient objects. How to integrate multi-level features becomes an open problem in saliency detection. In this paper, we propose a novel bi-directional message passing model to integrate multi-level features for salient object detection. At first, we adopt a Multi-scale Context-aware Feature Extraction Module (MCFEM) for multi-level feature maps to capture rich context information. Then a bi-directional structure is designed to pass messages between multi-level features, and a gate function is exploited to control the message passing rate. We use the features after message passing, which simultaneously encode semantic information and spatial details, to predict saliency maps. Finally, the predicted results are efficiently combined to generate the final saliency map. Quantitative and qualitative experiments on five benchmark datasets demonstrate that our proposed model performs favorably against the state-of-the-art methods under different evaluation metrics.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_A_Bi-Directional_Message_CVPR_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_A_Bi-Directional_Message_CVPR_2018_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"salient-object-detection","task_name":"RGB Salient Object Detection"},{"task_slug":"saliency-detection","task_name":"Saliency Detection"},{"task_slug":"salient-object-detection-1","task_name":"Salient Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/salient-object-detection-on-istd","task":"RGB Salient Object Detection","dataset":"ISTD","model":"BMPM","rank_in_archive_order":2,"of":7,"metrics":{"Balanced Error Rate":"7.10"},"uses_additional_data":false},{"leaderboard":"/sota/salient-object-detection-on-pascal-s","task":"RGB Salient Object Detection","dataset":"PASCAL-S","model":"BMPM","rank_in_archive_order":12,"of":13,"metrics":{"MAE":"0.074"},"uses_additional_data":false},{"leaderboard":"/sota/salient-object-detection-on-sbu","task":"RGB Salient Object Detection","dataset":"SBU / SBU-Refine","model":"BMPM","rank_in_archive_order":3,"of":7,"metrics":{"Balanced Error Rate":"6.17"},"uses_additional_data":false},{"leaderboard":"/sota/salient-object-detection-on-sod","task":"RGB Salient Object Detection","dataset":"SOD","model":"BMPM","rank_in_archive_order":2,"of":3,"metrics":{"MAE":"0.108"},"uses_additional_data":false},{"leaderboard":"/sota/salient-object-detection-on-ucf","task":"RGB Salient Object Detection","dataset":"UCF","model":"BMPM","rank_in_archive_order":3,"of":7,"metrics":{"Balanced Error Rate":"8.09"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}