{"id":"W4292873932","doi":"10.1109/mwscas54063.2022.9859341","title":"MorIRNet: A Deep Image Retrieval Network using Morphological Feature and Residual Block","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Convolutional neural network; Residual; Artificial intelligence; Benchmark (surveying); Block (permutation group theory); Image retrieval; Pattern recognition (psychology); Feature (linguistics); Deep learning; Feature extraction; Contextual image classification; Image (mathematics); Algorithm; Mathematics","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":[0.000956725,0.0003728685,0.0004418517,0.0002452954,0.0007232976,0.0005212658,0.0009328231,0.0001479416,0.00002845244],"category_scores_gemma":[0.00007099774,0.0003519657,0.00009896369,0.0005374771,0.0001184996,0.0005871252,0.0006441476,0.0007148061,0.00000836834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002626345,"about_ca_system_score_gemma":0.00006027188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002648989,"about_ca_topic_score_gemma":0.000002974457,"domain_scores_codex":[0.9966183,0.0003471611,0.0004726586,0.001021026,0.001026125,0.000514686],"domain_scores_gemma":[0.9985235,0.0002415351,0.0003396616,0.000505307,0.0001836702,0.000206294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001448176,0.00185954,0.01049255,0.0006724933,0.001434871,0.01177906,0.005999556,0.0754258,0.6292177,0.1609045,0.08656242,0.01420324],"study_design_scores_gemma":[0.007010994,0.006176982,0.006972359,0.0008297566,0.0002653816,0.03415638,0.001390081,0.6664679,0.02516659,0.0066544,0.2395214,0.005387725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6791287,0.008366634,0.2776484,0.007222678,0.01504515,0.002944768,0.0005094528,0.001332063,0.007802177],"genre_scores_gemma":[0.9932594,0.0003242836,0.002302218,0.0008708907,0.001181772,0.00007192188,0.00003678735,0.00004822527,0.001904557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6040512,"threshold_uncertainty_score":0.9998932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02381683306590218,"score_gpt":0.2753605592275453,"score_spread":0.2515437261616432,"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."}}