{"url":"/task/saliency-detection","name":"Saliency Detection","slug":"saliency-detection","description_markdown":"**Saliency Detection** is a preprocessing step in computer vision which aims at finding salient objects in an image.\n\n\n<span class=\"description-source\">Source: [An Unsupervised Game-Theoretic Approach to Saliency Detection ](https://arxiv.org/abs/1708.02476)</span>","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":364,"papers_with_code":137,"benchmarks":7,"benchmark_tables_in_archive":7,"benchmark_tables_shown":7,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":15,"subtasks":4,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/saliency-detection-on-dut-omron","slug":"saliency-detection-on-dut-omron","dataset":"DUT-OMRON","dataset_url":"/dataset/dut-omron","rows_in_archive":5,"metrics":["MAE","Fwβ","Sm","relaxFbβ","{max}Fβ"],"first_row_in_archive_order":{"model":"Pyramid Feature Attention","paper_title":"Pyramid Feature Attention Network for Saliency detection","paper_url":"/paper/pyramid-feature-selective-network-for","paper_date":"2019-03-01","arxiv_id":"1903.00179","code_links":[{"title":"CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection","url":"https://github.com/CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection"},{"title":"sairajk/PyTorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection","url":"https://github.com/sairajk/PyTorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection"},{"title":"halbielee/EPS","url":"https://github.com/halbielee/EPS"},{"title":"Wu0409/HSC_WSSS","url":"https://github.com/Wu0409/HSC_WSSS"},{"title":"ThorraySJTU/Pytorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection","url":"https://github.com/ThorraySJTU/Pytorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection"},{"title":"dizaiyoufang/pytorch_PFAN","url":"https://github.com/dizaiyoufang/pytorch_PFAN"}],"syntology":{"n":10,"n_ran":2,"n_unverified":8,"n_pointer_only":0}}},{"leaderboard":"/sota/saliency-detection-on-hku-is","slug":"saliency-detection-on-hku-is","dataset":"HKU-IS","dataset_url":"/dataset/hku-is","rows_in_archive":3,"metrics":["MAE","Fwβ","Sm","relaxFbβ","{max}Fβ"],"first_row_in_archive_order":{"model":"LDF(ours)","paper_title":"Label Decoupling Framework for Salient Object Detection","paper_url":"/paper/label-decoupling-framework-for-salient-object-1","paper_date":"2020-08-25","arxiv_id":"2008.11048","code_links":[{"title":"weijun88/LDF","url":"https://github.com/weijun88/LDF"}],"syntology":null}},{"leaderboard":"/sota/saliency-detection-on-cat2000","slug":"saliency-detection-on-cat2000","dataset":"CAT2000","dataset_url":"/dataset/cat2000","rows_in_archive":2,"metrics":["AUC","NSS"],"first_row_in_archive_order":{"model":"SUM","paper_title":"SUM: Saliency Unification through Mamba for Visual Attention Modeling","paper_url":"/paper/sum-saliency-unification-through-mamba-for","paper_date":"2024-06-25","arxiv_id":"2406.17815","code_links":[{"title":"Arhosseini77/SUM","url":"https://github.com/Arhosseini77/SUM"}],"syntology":{"n":14,"n_ran":14,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/saliency-detection-on-duts-test","slug":"saliency-detection-on-duts-test","dataset":"DUTS-test","dataset_url":"/dataset/duts","rows_in_archive":2,"metrics":["MAE"],"first_row_in_archive_order":{"model":"PFAN [zhao2019pyramid] (+) PRN","paper_title":"PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization","paper_url":"/paper/threshnet-segmentation-refinement-inspired-by","paper_date":"2022-11-12","arxiv_id":"2211.06560","code_links":[{"title":"savinay95n/PatchRefineNet","url":"https://github.com/savinay95n/PatchRefineNet"}],"syntology":null}},{"leaderboard":"/sota/saliency-detection-on-ecssd","slug":"saliency-detection-on-ecssd","dataset":"ECSSD","dataset_url":"/dataset/ecssd","rows_in_archive":1,"metrics":["MAE"],"first_row_in_archive_order":{"model":"Pyramid Feature Attention","paper_title":"Pyramid Feature Attention Network for Saliency detection","paper_url":"/paper/pyramid-feature-selective-network-for","paper_date":"2019-03-01","arxiv_id":"1903.00179","code_links":[{"title":"CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection","url":"https://github.com/CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection"},{"title":"sairajk/PyTorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection","url":"https://github.com/sairajk/PyTorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection"},{"title":"halbielee/EPS","url":"https://github.com/halbielee/EPS"},{"title":"Wu0409/HSC_WSSS","url":"https://github.com/Wu0409/HSC_WSSS"},{"title":"ThorraySJTU/Pytorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection","url":"https://github.com/ThorraySJTU/Pytorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection"},{"title":"dizaiyoufang/pytorch_PFAN","url":"https://github.com/dizaiyoufang/pytorch_PFAN"}],"syntology":{"n":10,"n_ran":2,"n_unverified":8,"n_pointer_only":0}}},{"leaderboard":"/sota/saliency-detection-on-pascal-context","slug":"saliency-detection-on-pascal-context","dataset":"PASCAL Context","dataset_url":"/dataset/pascal-context","rows_in_archive":1,"metrics":["max_F1"],"first_row_in_archive_order":{"model":"InvPT","paper_title":"InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding","paper_url":"/paper/inverted-pyramid-multi-task-transformer-for","paper_date":"2022-03-15","arxiv_id":"2203.07997","code_links":[{"title":"prismformore/InvPT","url":"https://github.com/prismformore/InvPT"}],"syntology":{"n":4,"n_ran":2,"n_unverified":2,"n_pointer_only":0}}},{"leaderboard":"/sota/saliency-detection-on-pascal-s","slug":"saliency-detection-on-pascal-s","dataset":"PASCAL-S","dataset_url":"/dataset/pascal-s","rows_in_archive":1,"metrics":["MAE"],"first_row_in_archive_order":{"model":"Pyramid Feature Attention","paper_title":"Pyramid Feature Attention Network for Saliency detection","paper_url":"/paper/pyramid-feature-selective-network-for","paper_date":"2019-03-01","arxiv_id":"1903.00179","code_links":[{"title":"CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection","url":"https://github.com/CaitinZhao/cvpr2019_Pyramid-Feature-Attention-Network-for-Saliency-detection"},{"title":"sairajk/PyTorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection","url":"https://github.com/sairajk/PyTorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection"},{"title":"halbielee/EPS","url":"https://github.com/halbielee/EPS"},{"title":"Wu0409/HSC_WSSS","url":"https://github.com/Wu0409/HSC_WSSS"},{"title":"ThorraySJTU/Pytorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection","url":"https://github.com/ThorraySJTU/Pytorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection"},{"title":"dizaiyoufang/pytorch_PFAN","url":"https://github.com/dizaiyoufang/pytorch_PFAN"}],"syntology":{"n":10,"n_ran":2,"n_unverified":8,"n_pointer_only":0}}}],"datasets":[{"url":"/dataset/pascal-context","name":"PASCAL Context","full_name":"","num_papers_in_archive":323},{"url":"/dataset/duts","name":"DUTS","full_name":"","num_papers_in_archive":286},{"url":"/dataset/pascal-s","name":"PASCAL-S","full_name":"","num_papers_in_archive":271},{"url":"/dataset/hku-is","name":"HKU-IS","full_name":"","num_papers_in_archive":226},{"url":"/dataset/dut-omron","name":"DUT-OMRON","full_name":"","num_papers_in_archive":214},{"url":"/dataset/isun","name":"iSUN","full_name":"iSUN","num_papers_in_archive":108},{"url":"/dataset/cat2000","name":"CAT2000","full_name":"","num_papers_in_archive":57},{"url":"/dataset/ecssd","name":"ECSSD","full_name":"Extended Complex Scene Saliency Dataset","num_papers_in_archive":34},{"url":"/dataset/redweb-s","name":"ReDWeb-S","full_name":null,"num_papers_in_archive":9},{"url":"/dataset/hs-sod","name":"HS-SOD","full_name":"HyperSpectral Salient Object Detection Dataset","num_papers_in_archive":8},{"url":"/dataset/lytro-illum","name":"Lytro Illum","full_name":"","num_papers_in_archive":6},{"url":"/dataset/come15k","name":"COME15K","full_name":"","num_papers_in_archive":5},{"url":"/dataset/usis10k","name":"USIS10K","full_name":"Large-scale Underwater Salient Instance Segmentation Dataset","num_papers_in_archive":4},{"url":"/dataset/avimos","name":"AViMoS","full_name":"Audio-Visual Mouse Saliency","num_papers_in_archive":1},{"url":"/dataset/cat","name":"CAT","full_name":"Context Adjustment Training","num_papers_in_archive":1}],"subtasks":[{"url":"/task/co-saliency-detection","name":"Co-Salient Object Detection"},{"url":"/task/saliency-prediction","name":"Saliency Prediction"},{"url":"/task/unsupervised-saliency-detection","name":"Unsupervised Saliency Detection"},{"url":"/task/video-saliency-detection","name":"Video Saliency Detection"}],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":137,"tagged_in_all":364,"items":[{"url":"/paper/u-2-net-going-deeper-with-nested-u-structure","title":"U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection","date":"2020-05-18","arxiv_id":"2005.09007","repositories_listed":29,"syntology":{"n":42,"n_ran":4,"n_unverified":38,"n_pointer_only":2}},{"url":"/paper/pyramid-feature-selective-network-for","title":"Pyramid Feature Attention Network for Saliency detection","date":"2019-03-01","arxiv_id":"1903.00179","repositories_listed":6,"syntology":{"n":10,"n_ran":2,"n_unverified":8,"n_pointer_only":0}},{"url":"/paper/sanity-checks-for-saliency-maps","title":"Sanity Checks for Saliency Maps","date":"2018-10-08","arxiv_id":"1810.03292","repositories_listed":5,"syntology":null},{"url":"/paper/p2t-pyramid-pooling-transformer-for-scene","title":"P2T: Pyramid Pooling Transformer for Scene Understanding","date":"2021-06-22","arxiv_id":"2106.12011","repositories_listed":4,"syntology":null},{"url":"/paper/icnet-intra-saliency-correlation-network-for","title":"ICNet: Intra-saliency Correlation Network for Co-Saliency Detection","date":"2020-12-01","arxiv_id":null,"repositories_listed":4,"syntology":null},{"url":"/paper/uncertainty-inspired-rgb-d-saliency-detection","title":"Uncertainty Inspired RGB-D Saliency Detection","date":"2020-09-07","arxiv_id":"2009.03075","repositories_listed":4,"syntology":null},{"url":"/paper/real-time-image-saliency-for-black-box","title":"Real Time Image Saliency for Black Box Classifiers","date":"2017-05-22","arxiv_id":"1705.07857","repositories_listed":4,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":3}},{"url":"/paper/deeply-supervised-salient-object-detection","title":"Deeply supervised salient object detection with short connections","date":"2016-11-15","arxiv_id":"1611.04849","repositories_listed":4,"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/a-weakly-supervised-learning-framework-for","title":"A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels","date":"2022-09-07","arxiv_id":"2209.02957","repositories_listed":3,"syntology":null},{"url":"/paper/specificity-preserving-rgb-d-saliency","title":"Specificity-preserving RGB-D Saliency Detection","date":"2021-08-18","arxiv_id":"2108.08162","repositories_listed":3,"syntology":{"n":14,"n_ran":8,"n_unverified":6,"n_pointer_only":14}},{"url":"/paper/cagnet-content-aware-guidance-for-salient","title":"CAGNet: Content-Aware Guidance for Salient Object Detection","date":"2019-11-29","arxiv_id":"1911.13168","repositories_listed":3,"syntology":null},{"url":"/paper/time-series-anomaly-detection-service-at","title":"Time-Series Anomaly Detection Service at Microsoft","date":"2019-06-10","arxiv_id":"1906.03821","repositories_listed":3,"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/joint-correcting-and-refinement-for-balanced","title":"Joint Correcting and Refinement for Balanced Low-Light Image Enhancement","date":"2023-09-28","arxiv_id":"2309.16128","repositories_listed":2,"syntology":null},{"url":"/paper/decoupled-diffusion-models-with-explicit","title":"Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation","date":"2023-06-23","arxiv_id":"2306.13720","repositories_listed":2,"syntology":null},{"url":"/paper/visual-saliency-transformer","title":"Visual Saliency Transformer","date":"2021-04-25","arxiv_id":"2104.12099","repositories_listed":2,"syntology":{"n":11,"n_ran":7,"n_unverified":4,"n_pointer_only":11}},{"url":"/paper/accurate-rgb-d-salient-object-detection-via","title":"Accurate RGB-D Salient Object Detection via Collaborative Learning","date":"2020-07-23","arxiv_id":"2007.11782","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_unverified":1,"n_pointer_only":2}},{"url":"/paper/unsupervised-discovery-of-interpretable","title":"Unsupervised Discovery of Interpretable Directions in the GAN Latent Space","date":"2020-02-10","arxiv_id":"2002.03754","repositories_listed":2,"syntology":null},{"url":"/paper/motion-guided-attention-for-video-salient","title":"Motion Guided Attention for Video Salient Object Detection","date":"2019-09-16","arxiv_id":"1909.07061","repositories_listed":2,"syntology":null},{"url":"/paper/light-field-saliency-detection-with-deep","title":"Light Field Saliency Detection with Deep Convolutional Networks","date":"2019-06-19","arxiv_id":"1906.08331","repositories_listed":2,"syntology":null},{"url":"/paper/picanet-pixel-wise-contextual-attention","title":"PiCANet: Pixel-wise Contextual Attention Learning for Accurate Saliency Detection","date":"2018-12-15","arxiv_id":"1812.06314","repositories_listed":2,"syntology":null},{"url":"/paper/hyperspectral-image-dataset-for-benchmarking","title":"Hyperspectral Image Dataset for Benchmarking on Salient Object Detection","date":"2018-06-29","arxiv_id":"1806.11314","repositories_listed":2,"syntology":{"n":5,"n_ran":0,"n_unverified":5,"n_pointer_only":0}},{"url":"/paper/pdnet-prior-model-guided-depth-enhanced","title":"PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection","date":"2018-03-23","arxiv_id":"1803.08636","repositories_listed":2,"syntology":null},{"url":"/paper/picanet-learning-pixel-wise-contextual","title":"PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection","date":"2017-08-21","arxiv_id":"1708.06433","repositories_listed":2,"syntology":null},{"url":"/paper/non-local-deep-features-for-salient-object","title":"Non-Local Deep Features for Salient Object Detection","date":"2017-07-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/a-deep-spatial-contextual-long-term-recurrent","title":"A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection","date":"2016-10-06","arxiv_id":"1610.01708","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":1}},{"url":"/paper/visual-saliency-detection-based-on-multiscale","title":"Visual Saliency Detection Based on Multiscale Deep CNN Features","date":"2016-09-07","arxiv_id":"1609.02077","repositories_listed":2,"syntology":null},{"url":"/paper/deep-saliency-with-encoded-low-level-distance","title":"Deep Saliency with Encoded Low level Distance Map and High Level Features","date":"2016-04-19","arxiv_id":"1604.05495","repositories_listed":2,"syntology":null},{"url":"/paper/inner-and-inter-label-propagation-salient","title":"Inner and Inter Label Propagation: Salient Object Detection in the Wild","date":"2015-05-27","arxiv_id":"1505.07192","repositories_listed":2,"syntology":null},{"url":"/paper/a-deep-learning-framework-for-visual","title":"A Deep Learning Framework for Visual Attention Prediction and Analysis of News Interfaces","date":"2025-03-21","arxiv_id":"2503.17212","repositories_listed":1,"syntology":null},{"url":"/paper/copy-move-detection-in-optical-microscopy-a","title":"Copy-Move Detection in Optical Microscopy: A Segmentation Network and A Dataset","date":"2024-12-13","arxiv_id":"2412.10258","repositories_listed":1,"syntology":null}],"syntology_records":10,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}