{"id":"W2137356988","doi":"10.1109/iccv.2013.319","title":"An Adaptive Descriptor Design for Object Recognition in the Wild","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pipeline (software); Cognitive neuroscience of visual object recognition; Pixel; Object (grammar); Kernel (algebra); Pattern recognition (psychology); Invariant (physics); Set (abstract data type); Image processing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001272314,0.0004943207,0.001102526,0.0008066114,0.0002820343,0.000797236,0.001768533,0.0008851785,0.001670951],"category_scores_gemma":[0.002567951,0.0003052447,0.0007322968,0.001119786,0.0006887882,0.001441343,0.0009045238,0.001025841,0.001012189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007247829,"about_ca_system_score_gemma":0.0008563162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003446268,"about_ca_topic_score_gemma":0.002567174,"domain_scores_codex":[0.9991302,0.0001653084,0.00007284529,0.0003016763,0.0002391797,0.00009076177],"domain_scores_gemma":[0.999062,0.0002014134,0.00007962269,0.0001931722,0.0004211911,0.00004251308],"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":[0.0003295241,0.000206939,0.00108296,0.0001844715,0.0001035899,0.00008679729,0.00008595379,0.1310453,0.07068014,0.008647965,0.004179911,0.7833666],"study_design_scores_gemma":[0.00001838046,0.0001278908,0.0006098687,0.000005739042,0.00001888415,0.00007013208,0.00002235095,0.9844327,0.01081591,0.002186925,0.001675137,0.00001614335],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01026874,0.0001962509,0.9886842,0.00004340979,0.00003460408,0.00004330542,0.00004000195,0.000454763,0.0002348134],"genre_scores_gemma":[0.3725125,0.000437682,0.6221578,0.0002050597,0.00009840466,0.0002949958,0.0007228068,0.0001516922,0.003419055],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003446268,"threshold_uncertainty_score":0.006852448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08230960480832816,"score_gpt":0.3019375382480531,"score_spread":0.2196279334397249,"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."}}