{"id":"W2803263450","doi":"10.1007/s11042-018-5959-8","title":"Video logo removal detection based on sparse representation","year":2018,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Tianjin City; China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Logo (programming language); Artificial intelligence; Representation (politics); Popularity; Prior probability; Computer vision; The Internet; Pattern recognition (psychology); Multimedia; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002893876,0.0006197317,0.00054598,0.002068877,0.0003210721,0.0006004929,0.0005202211,0.0006228434,0.00125071],"category_scores_gemma":[0.001467123,0.0002026451,0.000459256,0.001103974,0.0003320853,0.001012398,0.0007497207,0.0006963275,0.0006877879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001508537,"about_ca_system_score_gemma":0.000483684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009420212,"about_ca_topic_score_gemma":0.00148458,"domain_scores_codex":[0.9996642,0.00004020852,0.00001307136,0.00005033169,0.0001815415,0.00005068549],"domain_scores_gemma":[0.9994936,0.0001338649,0.00007719466,0.0000677307,0.0001963336,0.00003124794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003900622,0.0002038677,0.002816704,0.000166041,0.00004871802,0.0003162029,0.000103272,0.01679591,0.2126831,0.004718842,0.004214778,0.7575424],"study_design_scores_gemma":[0.00002298309,0.0001648942,0.005829532,0.00004277998,0.00007790817,0.0009112285,0.0001359729,0.8985668,0.08515683,0.004356095,0.004693443,0.00004146809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06012908,0.000322676,0.935355,0.0002813526,0.0001035966,0.00006315927,0.0001446105,0.0007245436,0.002875936],"genre_scores_gemma":[0.4956284,0.000991344,0.4966762,0.0002337998,0.0002303394,0.00009280573,0.0008653301,0.0001213419,0.005160462],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002068877,"threshold_uncertainty_score":0.004184067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02858618042671615,"score_gpt":0.2675505055514261,"score_spread":0.2389643251247099,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}