{"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/pyramid-feature-selective-network-for","title":"Pyramid Feature Attention Network for Saliency detection","arxiv_id":"1903.00179","date":"2019-03-01","proceeding":"CVPR 2019 6","authors":["Ting Zhao","Xiangqian Wu"],"abstract":"Saliency detection is one of the basic challenges in computer vision. How to\nextract effective features is a critical point for saliency detection. Recent\nmethods mainly adopt integrating multi-scale convolutional features\nindiscriminately. However, not all features are useful for saliency detection\nand some even cause interferences. To solve this problem, we propose Pyramid\nFeature Attention network to focus on effective high-level context features and\nlow-level spatial structural features. First, we design Context-aware Pyramid\nFeature Extraction (CPFE) module for multi-scale high-level feature maps to\ncapture rich context features. Second, we adopt channel-wise attention (CA)\nafter CPFE feature maps and spatial attention (SA) after low-level feature\nmaps, then fuse outputs of CA & SA together. Finally, we propose an edge\npreservation loss to guide network to learn more detailed information in\nboundary localization. Extensive evaluations on five benchmark datasets\ndemonstrate that the proposed method outperforms the state-of-the-art\napproaches under different evaluation metrics.","url_abs":"http://arxiv.org/abs/1903.00179v2","url_pdf":"http://arxiv.org/pdf/1903.00179v2.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":[{"paper_slug":"pyramid-feature-selective-network-for","repo_url":"https://github.com/CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"pyramid-feature-selective-network-for","repo_url":"https://github.com/ThorraySJTU/Pytorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"pyramid-feature-selective-network-for","repo_url":"https://github.com/Wu0409/HSC_WSSS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"pyramid-feature-selective-network-for","repo_url":"https://github.com/dizaiyoufang/pytorch_PFAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"pyramid-feature-selective-network-for","repo_url":"https://github.com/halbielee/EPS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"pyramid-feature-selective-network-for","repo_url":"https://github.com/sairajk/PyTorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"saliency-detection","task_name":"Saliency Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/saliency-detection-on-dut-omron","task":"Saliency Detection","dataset":"DUT-OMRON","model":"Pyramid Feature Attention","rank_in_archive_order":1,"of":5,"metrics":{"MAE":"0.0414"},"uses_additional_data":false},{"leaderboard":"/sota/saliency-detection-on-duts-test","task":"Saliency Detection","dataset":"DUTS-test","model":"Pyramid Feature Attention","rank_in_archive_order":2,"of":2,"metrics":{"MAE":"0.0405"},"uses_additional_data":false},{"leaderboard":"/sota/saliency-detection-on-ecssd","task":"Saliency Detection","dataset":"ECSSD","model":"Pyramid Feature Attention","rank_in_archive_order":1,"of":1,"metrics":{"MAE":"0.0328"},"uses_additional_data":false},{"leaderboard":"/sota/saliency-detection-on-hku-is","task":"Saliency Detection","dataset":"HKU-IS","model":"Pyramid Feature Attention","rank_in_archive_order":2,"of":3,"metrics":{"MAE":"0.0324"},"uses_additional_data":false},{"leaderboard":"/sota/saliency-detection-on-pascal-s","task":"Saliency Detection","dataset":"PASCAL-S","model":"Pyramid Feature Attention","rank_in_archive_order":1,"of":1,"metrics":{"MAE":"0.0677"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.00179","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.00179"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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