{"id":"W4413986970","doi":"10.14778/3748191.3748195","title":"Déjà Vu: Efficient Video-Language Query Engine with Learning-Based Inter-Frame Computation Reuse","year":2025,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Reuse; Déjà vu; Frame (networking); Computation; Artificial intelligence; Natural language processing; Programming language; Computer network; Engineering; Psychology","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":[0.0009684169,0.001464677,0.0008850747,0.0007565432,0.0004079165,0.00160184,0.00368672,0.001050707,0.005818575],"category_scores_gemma":[0.004496289,0.0005685488,0.0008574761,0.0007610458,0.0006027001,0.003654079,0.00277851,0.00159043,0.003189101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001009531,"about_ca_system_score_gemma":0.001436667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009119345,"about_ca_topic_score_gemma":0.01307997,"domain_scores_codex":[0.9991026,0.0001084722,0.00006105923,0.0002967246,0.0003348884,0.00009617276],"domain_scores_gemma":[0.9991986,0.000267158,0.00005433205,0.0002224574,0.000195727,0.00006179894],"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":[0.001254663,0.0004435887,0.002414418,0.0004719631,0.0001646816,0.0004448416,0.0003640252,0.09586532,0.06782362,0.02504184,0.07743357,0.7282776],"study_design_scores_gemma":[0.00007018345,0.0001162812,0.0001778118,0.00000984751,0.00001673627,0.0001106425,0.00005382994,0.9537489,0.02860084,0.007022225,0.01004251,0.00003021332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01673859,0.0006863781,0.9098299,0.0002685685,0.0001766341,0.0002097514,0.000969156,0.06856883,0.002552136],"genre_scores_gemma":[0.2655531,0.000485202,0.71236,0.0006143635,0.0001141907,0.000448302,0.006289948,0.003826482,0.01030854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009119345,"threshold_uncertainty_score":0.01946509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003793105079189405,"score_gpt":0.2416539932310142,"score_spread":0.2378608881518248,"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."}}