{"id":"W4313019099","doi":"10.1109/mmsp55362.2022.9949576","title":"Scalable Video Coding for Humans and Machines","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Bitstream; Computer science; Scalable Video Coding; Codec; Scalability; Artificial intelligence; Video tracking; Object detection; Coding (social sciences); Computer vision; Multiview Video Coding; Video processing; Decoding methods; Computer hardware; Pattern recognition (psychology); Database; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006624988,0.000277476,0.0002609191,0.0003543237,0.0009055625,0.0003945677,0.001197906,0.00006414275,0.0002001243],"category_scores_gemma":[0.0001954227,0.0002770064,0.0001064369,0.0004903532,0.00009483644,0.0008833033,0.0004371799,0.0005349398,0.000008235135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002030175,"about_ca_system_score_gemma":0.0001003274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006920009,"about_ca_topic_score_gemma":0.000002256192,"domain_scores_codex":[0.9975619,0.00007523404,0.0004157285,0.000755856,0.0007930201,0.0003982424],"domain_scores_gemma":[0.998547,0.0006041259,0.0002668369,0.0002487322,0.0002035285,0.0001298334],"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":[0.0002073631,0.0002146779,0.0003415402,0.00005438033,0.00004697152,0.00003148384,0.0007371387,0.001923201,0.009277783,0.002082961,0.007136368,0.9779461],"study_design_scores_gemma":[0.001193119,0.0002812579,0.0002393217,0.0001792246,0.00001845321,0.00004021727,0.0001579956,0.9342012,0.01765641,0.01277313,0.03268114,0.0005785194],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001539958,0.0005907844,0.9926195,0.002108619,0.0009318516,0.0004412599,0.00005178928,0.0003782256,0.001337981],"genre_scores_gemma":[0.7944716,0.0001133315,0.1970476,0.002568364,0.0006797218,0.0006237786,0.00007705191,0.00007031546,0.004348234],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9773676,"threshold_uncertainty_score":0.9999682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02808288834042201,"score_gpt":0.3176892961393225,"score_spread":0.2896064077989005,"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."}}