{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/saliency-prediction/papers/ran/1","list_of":"/task/saliency-prediction","task":"Saliency Prediction","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,20],"of":20,"counts":{"archive_papers_tagged":268,"with_a_code_link":105,"where_syntology_ran_a_sample":20,"not_listed_spam_title":0,"listed":268,"listed_where_code_ran":20,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":17,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":17,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/saliency-prediction/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/data-augmentation-via-latent-diffusion-for","slug":"data-augmentation-via-latent-diffusion-for","title":"Data Augmentation via Latent Diffusion for Saliency Prediction","date":"2024-09-11","arxiv_id":"2409.07307","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/data-augmentation-via-latent-diffusion-for#ran","syntology_url":"https://syntology.ai/paper/2409.07307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.07307"}},"official":{"repos":["ivrl/augsal"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sum-saliency-unification-through-mamba-for","slug":"sum-saliency-unification-through-mamba-for","title":"SUM: Saliency Unification through Mamba for Visual Attention Modeling","date":"2024-06-25","arxiv_id":"2406.17815","repositories_listed":1,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sum-saliency-unification-through-mamba-for#ran","syntology_url":"https://syntology.ai/paper/2406.17815","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.17815"}},"official":{"repos":["Arhosseini77/SUM"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/what-do-deep-saliency-models-learn-about-1","slug":"what-do-deep-saliency-models-learn-about-1","title":"What Do Deep Saliency Models Learn about Visual Attention?","date":"2023-10-14","arxiv_id":"2310.09679","repositories_listed":1,"syntology":{"n":14,"n_ran":8,"n_constructed":6,"n_ran_checked":7,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":14,"phrase":"8 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/what-do-deep-saliency-models-learn-about-1#ran","syntology_url":"https://syntology.ai/paper/2310.09679","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09679"}},"official":{"repos":["szzexpoi/saliency_analysis"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":6,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/panoramic-vision-transformer-for-saliency","slug":"panoramic-vision-transformer-for-saliency","title":"Panoramic Vision Transformer for Saliency Detection in 360° Videos","date":"2022-09-19","arxiv_id":"2209.08956","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/panoramic-vision-transformer-for-saliency#ran","syntology_url":"https://syntology.ai/paper/2209.08956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.08956"}},"official":{"repos":["hs-yn/paver"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/transalnet-visual-saliency-prediction-using","slug":"transalnet-visual-saliency-prediction-using","title":"TranSalNet: Towards perceptually relevant visual saliency prediction","date":"2021-10-07","arxiv_id":"2110.03593","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/transalnet-visual-saliency-prediction-using#ran","syntology_url":"https://syntology.ai/paper/2110.03593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.03593"}},"official":{"repos":["ljovo/transalnet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/specificity-preserving-rgb-d-saliency","slug":"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_constructed":5,"n_ran_checked":8,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":14,"phrase":"8 ran (of which 5 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/specificity-preserving-rgb-d-saliency#ran","syntology_url":"https://syntology.ai/paper/2108.08162","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.08162"}},"official":{"repos":["taozh2017/RGBD-SODsurvey","taozh2017/spnet","nnizhang/SMAC"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":5,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/energy-based-generative-cooperative-saliency","slug":"energy-based-generative-cooperative-saliency","title":"Energy-Based Generative Cooperative Saliency Prediction","date":"2021-06-25","arxiv_id":"2106.13389","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/energy-based-generative-cooperative-saliency#ran","syntology_url":"https://syntology.ai/paper/2106.13389","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.13389"}},"official":{"repos":["JingZhang617/SalCoopNets"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/calibrated-prediction-in-and-out-of-domain","slug":"calibrated-prediction-in-and-out-of-domain","title":"DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling","date":"2021-05-26","arxiv_id":"2105.12441","repositories_listed":2,"syntology":{"n":20,"n_ran":13,"n_constructed":9,"n_ran_checked":9,"n_instrument":4,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":16,"phrase":"13 ran (of which 9 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 4 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/calibrated-prediction-in-and-out-of-domain#ran","syntology_url":"https://syntology.ai/paper/2105.12441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.12441"}},"official":{"repos":["matthias-k/DeepGaze"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":9,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/rskdd-net-random-sample-based-keypoint","slug":"rskdd-net-random-sample-based-keypoint","title":"RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor","date":"2020-10-23","arxiv_id":"2010.12394","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/rskdd-net-random-sample-based-keypoint#ran","syntology_url":"https://syntology.ai/paper/2010.12394","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12394"}},"official":{"repos":["ispc-lab/RSKDD-Net"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fastsal-a-computationally-efficient-network","slug":"fastsal-a-computationally-efficient-network","title":"FastSal: a Computationally Efficient Network for Visual Saliency Prediction","date":"2020-08-25","arxiv_id":"2008.11151","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fastsal-a-computationally-efficient-network#ran","syntology_url":"https://syntology.ai/paper/2008.11151","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.11151"}},"official":{"repos":["feiyanhu/FastSal"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-segmentation-of-underwater-imagery","slug":"semantic-segmentation-of-underwater-imagery","title":"Semantic Segmentation of Underwater Imagery: Dataset and Benchmark","date":"2020-04-02","arxiv_id":"2004.01241","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semantic-segmentation-of-underwater-imagery#ran","syntology_url":"https://syntology.ai/paper/2004.01241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.01241"}},"official":null}},{"url":"/paper/unified-image-and-video-saliency-modeling","slug":"unified-image-and-video-saliency-modeling","title":"Unified Image and Video Saliency Modeling","date":"2020-03-11","arxiv_id":"2003.05477","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unified-image-and-video-saliency-modeling#ran","syntology_url":"https://syntology.ai/paper/2003.05477","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.05477"}},"official":{"repos":["rdroste/unisal"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/tidying-deep-saliency-prediction","slug":"tidying-deep-saliency-prediction","title":"Tidying Deep Saliency Prediction Architectures","date":"2020-03-10","arxiv_id":"2003.04942","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tidying-deep-saliency-prediction#ran","syntology_url":"https://syntology.ai/paper/2003.04942","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.04942"}},"official":{"repos":["samyak0210/saliency"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/direction-concentration-learning-enhancing","slug":"direction-concentration-learning-enhancing","title":"Direction Concentration Learning: Enhancing Congruency in Machine Learning","date":"2019-12-17","arxiv_id":"1912.08136","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/direction-concentration-learning-enhancing#ran","syntology_url":"https://syntology.ai/paper/1912.08136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.08136"}},"official":{"repos":["luoyan407/congruency"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/understanding-and-visualizing-deep-visual","slug":"understanding-and-visualizing-deep-visual","title":"Understanding and Visualizing Deep Visual Saliency Models","date":"2019-03-06","arxiv_id":"1903.02501","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/understanding-and-visualizing-deep-visual#ran","syntology_url":"https://syntology.ai/paper/1903.02501","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.02501"}},"official":{"repos":["SenHe/uavdvsm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/contextual-encoder-decoder-network-for-visual","slug":"contextual-encoder-decoder-network-for-visual","title":"Contextual Encoder-Decoder Network for Visual Saliency Prediction","date":"2019-02-18","arxiv_id":"1902.06634","repositories_listed":4,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/contextual-encoder-decoder-network-for-visual#ran","syntology_url":"https://syntology.ai/paper/1902.06634","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.06634"}},"official":{"repos":["alexanderkroner/saliency"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"url":"/paper/faster-gaze-prediction-with-dense-networks","slug":"faster-gaze-prediction-with-dense-networks","title":"Faster gaze prediction with dense networks and Fisher pruning","date":"2018-01-17","arxiv_id":"1801.05787","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/faster-gaze-prediction-with-dense-networks#ran","syntology_url":"https://syntology.ai/paper/1801.05787","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.05787"}},"official":null}},{"url":"/paper/salgan-visual-saliency-prediction-with","slug":"salgan-visual-saliency-prediction-with","title":"SalGAN: Visual Saliency Prediction with Generative Adversarial Networks","date":"2017-01-04","arxiv_id":"1701.01081","repositories_listed":4,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/salgan-visual-saliency-prediction-with#ran","syntology_url":"https://syntology.ai/paper/1701.01081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.01081"}},"official":{"repos":["imatge-upc/salgan"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/predicting-human-eye-fixations-via-an-lstm","slug":"predicting-human-eye-fixations-via-an-lstm","title":"Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model","date":"2016-11-29","arxiv_id":"1611.09571","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/predicting-human-eye-fixations-via-an-lstm#ran","syntology_url":"https://syntology.ai/paper/1611.09571","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.09571"}},"official":{"repos":["marcellacornia/sam"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/a-deep-multi-level-network-for-saliency","slug":"a-deep-multi-level-network-for-saliency","title":"A Deep Multi-Level Network for Saliency Prediction","date":"2016-09-05","arxiv_id":"1609.01064","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-deep-multi-level-network-for-saliency#ran","syntology_url":"https://syntology.ai/paper/1609.01064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.01064"}},"official":{"repos":["marcellacornia/mlnet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}}],"record_sha256":"7b558ed682364906b4ab01d5e4095e3efd9ccf6c9723bdfa6514d9e65888f90b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}