{"id":"W4393063810","doi":"10.48550/arxiv.2403.13747","title":"Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; New York University Abu Dhabi","keywords":"Hash function; Computer science; Image (mathematics); Artificial intelligence; Image retrieval; Resolution (logic); Computer vision; Pattern recognition (psychology); Information retrieval; Computer security","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001044847,0.0009017804,0.001112756,0.001248671,0.0003154513,0.0009878249,0.001444186,0.0008713748,0.003999532],"category_scores_gemma":[0.002511511,0.0003546354,0.000538847,0.001341483,0.0005030718,0.003622663,0.001601037,0.001057825,0.002778925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006015647,"about_ca_system_score_gemma":0.0006122916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001934883,"about_ca_topic_score_gemma":0.003427453,"domain_scores_codex":[0.9994159,0.00009278305,0.00004070606,0.0001464565,0.0002194363,0.00008475397],"domain_scores_gemma":[0.9993786,0.0001500505,0.00008721207,0.0002153669,0.0001335155,0.00003525024],"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":[0.0004896213,0.0003571071,0.002289905,0.0004092545,0.000184518,0.0001947397,0.0001182671,0.0842199,0.08819457,0.009773646,0.01527745,0.7984912],"study_design_scores_gemma":[0.00006954565,0.0003250145,0.001466514,0.00003470244,0.00007232175,0.0004471763,0.00007296626,0.9317552,0.04793366,0.01048391,0.007284222,0.00005473254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1004094,0.003501826,0.8830433,0.0004142262,0.0002052522,0.0002409344,0.0008357962,0.005192148,0.006157053],"genre_scores_gemma":[0.6092765,0.001577615,0.376224,0.0005019417,0.0002124929,0.0001361151,0.003002617,0.0002052511,0.008863452],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003999532,"threshold_uncertainty_score":0.01337975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04390179177865572,"score_gpt":0.2212832001619874,"score_spread":0.1773814083833317,"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."}}