{"id":"W4395955659","doi":"10.55041/ijsrem31987","title":"IMAGE CAPTION GENERATOR USING DEEP LEARNING","year":2024,"lang":"en","type":"article","venue":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Convolutional neural network; Closed captioning; Artificial intelligence; Encoder; Generator (circuit theory); Deep learning; Image (mathematics); Convolution (computer science); Task (project management); Pattern recognition (psychology); Computer vision; Artificial neural network","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":[0.0005722113,0.00131131,0.000589665,0.0008294485,0.0003477723,0.001033875,0.00145649,0.001181649,0.01351963],"category_scores_gemma":[0.002346247,0.0004061282,0.000959639,0.0007539418,0.0004414669,0.001835429,0.0009204618,0.001445513,0.005843688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008532656,"about_ca_system_score_gemma":0.0005916954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001865593,"about_ca_topic_score_gemma":0.001832303,"domain_scores_codex":[0.9996872,0.00006001684,0.00002122952,0.0001207881,0.00007708791,0.00003375059],"domain_scores_gemma":[0.9992021,0.0002622123,0.0000622494,0.0001594038,0.0002706803,0.00004345618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003535161,0.0002226663,0.0007974133,0.000851903,0.0001248198,0.0007178989,0.0002454739,0.07218205,0.06662682,0.01021371,0.06086931,0.7867944],"study_design_scores_gemma":[0.00003456578,0.000168154,0.0005355217,0.00006956305,0.00004765507,0.0004087005,0.00009107025,0.8642085,0.09188762,0.01241175,0.03009004,0.00004693352],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01719322,0.0008946342,0.9273269,0.0005001863,0.0007957254,0.0006252084,0.002331828,0.0391856,0.01114671],"genre_scores_gemma":[0.2369502,0.0009718136,0.7297882,0.0004889359,0.0002927318,0.00069522,0.009328623,0.001760797,0.01972342],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01351963,"threshold_uncertainty_score":0.04522771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03904444950279942,"score_gpt":0.3576777065794045,"score_spread":0.3186332570766051,"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."}}