{"id":"W3135446078","doi":"10.1109/tcsvt.2021.3063001","title":"Feature Aggregation Networks Based on Dual Attention Capsules for Visual Object Tracking","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"China Scholarship Council; National Natural Science Foundation of China; Compute Canada","keywords":"Computer science; Artificial intelligence; Discriminative model; Convolutional neural network; Pattern recognition (psychology); Feature (linguistics); Histogram of oriented gradients; Feature vector; BitTorrent tracker; Feature learning; Feature extraction; Video tracking; Computer vision; Eye tracking; Histogram; Object (grammar); Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005895231,0.0009451169,0.0008431551,0.000692157,0.0003443883,0.0005966742,0.001515969,0.0006562305,0.001346921],"category_scores_gemma":[0.001554798,0.0003551595,0.0006056167,0.0008267364,0.0004944439,0.001561187,0.001505983,0.0009565664,0.0004492187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009978042,"about_ca_system_score_gemma":0.0007355122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007829663,"about_ca_topic_score_gemma":0.008435774,"domain_scores_codex":[0.9996004,0.00003910043,0.00002159673,0.0001565601,0.0001113536,0.00007120029],"domain_scores_gemma":[0.9995229,0.0001247622,0.00008773558,0.0001051946,0.000114937,0.00004447427],"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":[0.0004587437,0.0001990399,0.004242103,0.0001104481,0.0001418343,0.0002249762,0.0001588808,0.2784295,0.05388846,0.00733816,0.004641263,0.6501666],"study_design_scores_gemma":[0.000008289006,0.00009331662,0.0008217735,0.00000462836,0.00002591492,0.00004694076,0.000008425041,0.98944,0.007018893,0.001725801,0.0007970965,0.000008836266],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0473874,0.0004732002,0.9475945,0.0001028408,0.0000562164,0.00005908144,0.0001061573,0.002498596,0.001721954],"genre_scores_gemma":[0.8372209,0.0002746896,0.1570972,0.0002240211,0.00005921138,0.0001067403,0.0005370754,0.0001505361,0.004329538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007829663,"threshold_uncertainty_score":0.0155682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02583028711931734,"score_gpt":0.2878912115274279,"score_spread":0.2620609244081105,"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."}}