{"id":"W4402775761","doi":"10.1109/cvpr52733.2024.01497","title":"Transferable and Principled Efficiency for Open-Vocabulary Segmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; Segmentation; Vocabulary; Artificial intelligence; Natural language processing; Linguistics","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":[0.002059314,0.00185794,0.001527066,0.001447022,0.001046483,0.003310778,0.004091047,0.002366109,0.008664633],"category_scores_gemma":[0.0094183,0.001019475,0.00159852,0.001543415,0.001968011,0.005900467,0.004565234,0.002781861,0.007130724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001535549,"about_ca_system_score_gemma":0.002273795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00648696,"about_ca_topic_score_gemma":0.01015057,"domain_scores_codex":[0.9977976,0.0004242457,0.0001527449,0.0007507012,0.0005829319,0.0002917709],"domain_scores_gemma":[0.9970161,0.001224221,0.0001376644,0.00109701,0.0003853604,0.0001397689],"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":[0.0005000763,0.0002916025,0.002173648,0.0007202902,0.0001726726,0.0004668887,0.0008667069,0.1967637,0.05912578,0.058148,0.02843461,0.6523361],"study_design_scores_gemma":[0.00005917385,0.0001064257,0.0003950855,0.00006025222,0.00004000407,0.0002771124,0.0002182302,0.874593,0.02771551,0.08322646,0.01326311,0.00004560094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02057528,0.000822101,0.9501078,0.0004057898,0.0001873806,0.0001611967,0.0006524951,0.01880023,0.008287673],"genre_scores_gemma":[0.3201851,0.0005602555,0.663505,0.0005740111,0.0001461101,0.0003257182,0.004056237,0.004887388,0.005760305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008664633,"threshold_uncertainty_score":0.0289861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01834370040238851,"score_gpt":0.3150522730493097,"score_spread":0.2967085726469212,"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."}}