{"id":"W3122590452","doi":"10.1109/tcsvt.2021.3054062","title":"Stereoscopic Image Retargeting Based on Deep Convolutional Neural Network","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Science Foundation of Tianjin City; National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Retargeting; Stereoscopy; Artificial intelligence; Seam carving; Computer vision; Computer science; Convolutional neural network; Consistency (knowledge bases); Salient; Deep learning; Depth map; Visualization; Image (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":[],"consensus_categories":[],"category_scores_codex":[0.0002661035,0.0001762022,0.000256944,0.0002714707,0.0005220647,0.0001432545,0.0002263573,0.0001817199,0.0000126487],"category_scores_gemma":[0.00002050286,0.0001744043,0.0001115748,0.0006118523,0.00007796599,0.0001858602,0.000003386146,0.0002645088,0.00001557953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006484584,"about_ca_system_score_gemma":0.00005967759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008877323,"about_ca_topic_score_gemma":0.00002294808,"domain_scores_codex":[0.9984196,0.0001063271,0.000355929,0.0005524934,0.0002063324,0.0003592576],"domain_scores_gemma":[0.9990698,0.0001570565,0.0001097613,0.0003936041,0.00019481,0.00007493742],"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":[0.0001665469,0.00176876,0.001448588,0.001245508,0.0004772464,0.0002611238,0.000446448,0.2661213,0.0729271,0.1886163,0.001882372,0.4646387],"study_design_scores_gemma":[0.001081771,0.0006197309,0.0002195807,0.0001260138,0.00002421704,0.0001517043,0.0001018502,0.986801,0.006902054,0.001265803,0.002422507,0.0002837702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01903136,0.0002678788,0.9764523,0.001006759,0.002310128,0.0003481088,0.00001435346,0.0004025048,0.0001666387],"genre_scores_gemma":[0.9976143,0.00001185497,0.001571588,0.0003614089,0.00006730371,0.0001662485,0.000003232916,0.00001509716,0.0001889619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9785829,"threshold_uncertainty_score":0.7111999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02118816247447714,"score_gpt":0.2583497536545451,"score_spread":0.237161591180068,"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."}}