{"id":"W4399203220","doi":"10.1145/3649902.3653351","title":"CSA-CNN: A Contrastive Self-Attention Neural Network for Pupil Segmentation in Eye Gaze Tracking","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Huawei Technologies (Canada)","funders":"","keywords":"Gaze; Computer science; Eye tracking; Artificial intelligence; Pupil; Computer vision; Segmentation; Tracking (education); Convolutional neural network; Psychology; Neuroscience","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":[0.0003449983,0.000135896,0.0001515322,0.0001570795,0.00008503292,0.0002379424,0.0002687707,0.00008469918,0.000007429984],"category_scores_gemma":[0.00002314196,0.0001229444,0.00007641822,0.0005387394,0.00002609859,0.0004730974,0.00005184444,0.0001711622,0.00002259279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009403915,"about_ca_system_score_gemma":0.00003544757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001841691,"about_ca_topic_score_gemma":0.00004000864,"domain_scores_codex":[0.9987903,0.00004891464,0.0002433672,0.0004396391,0.0001222291,0.0003555023],"domain_scores_gemma":[0.9995219,0.0001860754,0.00004630882,0.0001537631,0.00005836646,0.00003362203],"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":[0.00003474058,0.0002358723,0.02544699,0.0001604051,0.0001222603,0.0001087896,0.001101859,0.00278168,0.009271194,0.2789206,0.003870812,0.6779448],"study_design_scores_gemma":[0.0008678818,0.0002709421,0.1012874,0.0001694479,0.00002770225,0.00001768995,0.0001534023,0.8821596,0.002079492,0.01136988,0.001273171,0.0003233702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1221741,0.0003422876,0.8723777,0.002273903,0.0008534203,0.000394818,0.000002644811,0.001166237,0.0004148795],"genre_scores_gemma":[0.9490236,0.000009946731,0.05036781,0.0001559132,0.0001121175,0.00009601873,0.000008308813,0.0000109642,0.0002152992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8793779,"threshold_uncertainty_score":0.5013528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617807916907508,"score_gpt":0.2810789525591908,"score_spread":0.2649008733901157,"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."}}