{"meta":{"query_hash":"8362d9887ee7","filters":{"venue":"2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/8362d9887ee7","api":"https://metacan.xera.ac/api/v1/cohort?venue=2021+16th+IEEE+International+Conference+on+Automatic+Face+and+Gesture+Recognition+%28FG+2021%29"},"results":[{"id":"W3211666705","doi":"10.1109/fg52635.2021.9667055","title":"Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition","year":2021,"lang":"en","type":"article","venue":"2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Modalities; Computer science; Leverage (statistics); Emotion recognition; Valence (chemistry); Affective computing; Salient; Arousal; Fusion; Speech recognition; Artificial intelligence; Modal; Feature (linguistics); Focus (optics); Pattern recognition (psychology); Machine learning; Psychology","score_opus":0.07865010546663687,"score_gpt":0.3726191346843099,"score_spread":0.29396902921767304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211666705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04769511,0.0024677245,0.9380324,0.00030180762,0.0002476798,0.00011617479,0.00060291414,0.0044668103,0.006069314],"genre_scores_gemma":[0.749583,0.0013585249,0.24002604,0.0003793645,0.00017490645,0.00018361864,0.0018524923,0.00040029426,0.0060418136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999613,0.000058567155,0.000019021516,0.00012622318,0.00011067512,0.00007251063],"domain_scores_gemma":[0.9997166,0.00009559678,0.000027410624,0.000042128642,0.000097013995,0.00002131005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000809221,0.00102208,0.0006554861,0.0008748686,0.0002996452,0.00076083187,0.00086260115,0.0005943216,0.0042346027],"category_scores_gemma":[0.0017242515,0.00025478017,0.001099389,0.00070697267,0.00030795057,0.0012936181,0.0015926029,0.0009876977,0.0013107761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055101985,0.00021538035,0.0019944715,0.00021409507,0.00021759394,0.00015449084,0.00019322465,0.08641972,0.079827346,0.0054465663,0.008150248,0.8166159],"study_design_scores_gemma":[0.000013128525,0.00011253926,0.002992469,0.00003269914,0.00008428784,0.00011378745,0.000060227812,0.96316695,0.02132142,0.007909896,0.0041592657,0.00003327679],"about_ca_topic_score_codex":0.0055493014,"about_ca_topic_score_gemma":0.0069430424,"teacher_disagreement_score":0.0055493014,"about_ca_system_score_codex":0.00060629845,"about_ca_system_score_gemma":0.00037827043,"threshold_uncertainty_score":0.014166176},"labels":[],"label_agreement":null},{"id":"W4210702106","doi":"10.1109/fg52635.2021.9666986","title":"Face Trees for Expression Recognition","year":2021,"lang":"en","type":"article","venue":"2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Tree traversal; Computer science; Landmark; Artificial intelligence; Expression (computer science); Embedding; Pattern recognition (psychology); Tree (set theory); Face (sociological concept); Architecture; Scale (ratio); Component (thermodynamics); Host (biology); Facial recognition system; Facial expression; Algorithm; Mathematics","score_opus":0.09484208444964427,"score_gpt":0.3551049256135598,"score_spread":0.2602628411639155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210702106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010718198,0.0008133076,0.9714706,0.00031289723,0.0001686136,0.00010088216,0.00082815834,0.01037576,0.0052116034],"genre_scores_gemma":[0.3479706,0.001469456,0.6192843,0.0008375946,0.00024653153,0.0004323356,0.0067787804,0.0007966609,0.022183781],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994941,0.000072789866,0.000016585678,0.00017158106,0.00017128972,0.000073618816],"domain_scores_gemma":[0.9997155,0.00007001424,0.00002020269,0.00007832874,0.00009409269,0.00002173889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000493733,0.0011456982,0.00070737366,0.0007924545,0.00035231566,0.0008696007,0.0015123448,0.0009310082,0.010751674],"category_scores_gemma":[0.0014498167,0.0003797799,0.0009438544,0.0007010191,0.00029068693,0.0016528222,0.0010444212,0.0016507541,0.008590142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019656816,0.00016704811,0.0010081364,0.00011122531,0.00006654716,0.000102602055,0.00008323458,0.04135031,0.034137797,0.010696637,0.03262112,0.8794587],"study_design_scores_gemma":[0.000011948783,0.000076993354,0.0008784188,0.00002720126,0.000026807573,0.00015452593,0.00003441668,0.944765,0.018086625,0.022701044,0.013215578,0.000021497886],"about_ca_topic_score_codex":0.0032600113,"about_ca_topic_score_gemma":0.005237182,"teacher_disagreement_score":0.010751674,"about_ca_system_score_codex":0.0005859821,"about_ca_system_score_gemma":0.00054289214,"threshold_uncertainty_score":0.035967886},"labels":[],"label_agreement":null},{"id":"W4225984337","doi":"10.1109/fg52635.2021.9666994","title":"The Many Faces of Anger: A Multicultural Video Dataset of Negative Emotions in the Wild (MFA-Wild)","year":2021,"lang":"en","type":"article","venue":"2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anger; Multiculturalism; Psychology; Computer science; Social psychology","score_opus":0.05518946392789806,"score_gpt":0.3148131111698275,"score_spread":0.25962364724192943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225984337","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41871312,0.004046672,0.016079612,0.0016420273,0.0021675278,0.0014053405,0.5150656,0.005487275,0.035392847],"genre_scores_gemma":[0.2833032,0.0010533793,0.03754967,0.0010248605,0.00042113836,0.0012291871,0.6592298,0.0005835911,0.015605138],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915075,0.00018341263,0.00006156762,0.00021739407,0.00023722323,0.00014953694],"domain_scores_gemma":[0.9989957,0.00015423642,0.0001048475,0.00019854747,0.0003630723,0.00018354092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074398675,0.0013267,0.000523616,0.001387477,0.001076084,0.0008481519,0.00083898724,0.0013502126,0.0036035671],"category_scores_gemma":[0.0020195548,0.00018949632,0.00067704933,0.00088331517,0.0004946591,0.0008872409,0.0014363689,0.0010965529,0.0042438637],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015041253,0.0009493395,0.04983196,0.0022175726,0.0003645764,0.0020710966,0.003915411,0.0019014432,0.049827628,0.002206187,0.70983857,0.17537206],"study_design_scores_gemma":[0.00016760729,0.00070917665,0.3958073,0.0007953692,0.00019613579,0.0047898153,0.010270567,0.02299063,0.028214173,0.002571171,0.53315884,0.0003291599],"about_ca_topic_score_codex":0.016530154,"about_ca_topic_score_gemma":0.061395604,"teacher_disagreement_score":0.016530154,"about_ca_system_score_codex":0.00073874707,"about_ca_system_score_gemma":0.00042230758,"threshold_uncertainty_score":0.03286791},"labels":[],"label_agreement":null}]}