{"id":"W1924814796","doi":"10.1109/tmm.2015.2485538","title":"Guest Editorial: Deep Learning for Multimedia Computing","year":2015,"lang":"en","type":"editorial","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Multimedia; Deep learning; Artificial intelligence","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":[0.003648751,0.001670271,0.0017148,0.002176834,0.001564566,0.004923239,0.002112754,0.007798642,0.01852184],"category_scores_gemma":[0.01147727,0.00056614,0.001462685,0.0007800077,0.001524985,0.003315871,0.001518364,0.01168974,0.0125154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001605683,"about_ca_system_score_gemma":0.001636803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005131055,"about_ca_topic_score_gemma":0.00151576,"domain_scores_codex":[0.9978787,0.0002997691,0.0001807414,0.000253606,0.001169943,0.0002173954],"domain_scores_gemma":[0.9921663,0.002573789,0.0004891546,0.0001785138,0.002991328,0.001600994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002812846,0.00000764117,0.00002094958,0.00008938338,0.000009207846,0.00006066976,0.000004498773,0.0000271245,0.00005639527,0.0004142125,0.9914827,0.007799101],"study_design_scores_gemma":[0.00004773712,0.00002388169,0.0001554247,0.0002465563,0.00002438245,0.0002174387,0.00001700698,0.0003276595,0.0002012977,0.001631731,0.997093,0.00001386406],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00009552756,0.008565164,0.0008013175,0.07202916,0.9159993,0.00002415983,0.0001133984,0.0001625197,0.002209536],"genre_scores_gemma":[0.001159022,0.006937373,0.0003089552,0.02484871,0.9509394,0.00003371955,0.00008438894,0.00007881255,0.01560965],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.01852184,"threshold_uncertainty_score":0.06196177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01456618661103415,"score_gpt":0.274321100272386,"score_spread":0.2597549136613518,"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."}}