{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:H37ZYMZO4ARKHYRO4VOICG6JVL","short_pith_number":"pith:H37ZYMZO","schema_version":"1.0","canonical_sha256":"3eff9c332ee022a3e22ee55c811bc9aaee8a1770a9bca064e31388460b6cb181","source":{"kind":"arxiv","id":"2303.04554","version":1},"attestation_state":"computed","paper":{"title":"RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.CV","authors_text":"Bernard De Baets, Kallil M. Zielinski, Leonardo Scabini, Lucas C. Ribas, Odemir M. Bruno, Wesley N. Gon\\c{c}alves","submitted_at":"2023-03-08T13:09:03Z","abstract_excerpt":"Texture analysis is a classical yet challenging task in computer vision for which deep neural networks are actively being applied. Most approaches are based on building feature aggregation modules around a pre-trained backbone and then fine-tuning the new architecture on specific texture recognition tasks. Here we propose a new method named \\textbf{R}andom encoding of \\textbf{A}ggregated \\textbf{D}eep \\textbf{A}ctivation \\textbf{M}aps (RADAM) which extracts rich texture representations without ever changing the backbone. The technique consists of encoding the output at different depths of a pr"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2303.04554","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-08T13:09:03Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"344dd6ed3149f55ecce8f21f2f6594b9b10891d7a9e75bf44f97a106789c72d3","abstract_canon_sha256":"08d58fc3ddf3ab3cbe2f4fce31130f5e4a7d51e90e28db4750e9ccc28c26a91d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:49:22.709130Z","signature_b64":"qmwQ4jyl0fGzzJzgh0PfWY8MMRUF2boUB1QgR9P0Fb8Sbp85jKd/hcVU6gPtfE5QmYCezbCYG0PDHzVvUj6gCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3eff9c332ee022a3e22ee55c811bc9aaee8a1770a9bca064e31388460b6cb181","last_reissued_at":"2026-07-05T05:49:22.708771Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:49:22.708771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.CV","authors_text":"Bernard De Baets, Kallil M. Zielinski, Leonardo Scabini, Lucas C. Ribas, Odemir M. Bruno, Wesley N. Gon\\c{c}alves","submitted_at":"2023-03-08T13:09:03Z","abstract_excerpt":"Texture analysis is a classical yet challenging task in computer vision for which deep neural networks are actively being applied. Most approaches are based on building feature aggregation modules around a pre-trained backbone and then fine-tuning the new architecture on specific texture recognition tasks. Here we propose a new method named \\textbf{R}andom encoding of \\textbf{A}ggregated \\textbf{D}eep \\textbf{A}ctivation \\textbf{M}aps (RADAM) which extracts rich texture representations without ever changing the backbone. The technique consists of encoding the output at different depths of a pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.04554","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.04554/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2303.04554","created_at":"2026-07-05T05:49:22.708826+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.04554v1","created_at":"2026-07-05T05:49:22.708826+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.04554","created_at":"2026-07-05T05:49:22.708826+00:00"},{"alias_kind":"pith_short_12","alias_value":"H37ZYMZO4ARK","created_at":"2026-07-05T05:49:22.708826+00:00"},{"alias_kind":"pith_short_16","alias_value":"H37ZYMZO4ARKHYRO","created_at":"2026-07-05T05:49:22.708826+00:00"},{"alias_kind":"pith_short_8","alias_value":"H37ZYMZO","created_at":"2026-07-05T05:49:22.708826+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H37ZYMZO4ARKHYRO4VOICG6JVL","json":"https://pith.science/pith/H37ZYMZO4ARKHYRO4VOICG6JVL.json","graph_json":"https://pith.science/api/pith-number/H37ZYMZO4ARKHYRO4VOICG6JVL/graph.json","events_json":"https://pith.science/api/pith-number/H37ZYMZO4ARKHYRO4VOICG6JVL/events.json","paper":"https://pith.science/paper/H37ZYMZO"},"agent_actions":{"view_html":"https://pith.science/pith/H37ZYMZO4ARKHYRO4VOICG6JVL","download_json":"https://pith.science/pith/H37ZYMZO4ARKHYRO4VOICG6JVL.json","view_paper":"https://pith.science/paper/H37ZYMZO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.04554&json=true","fetch_graph":"https://pith.science/api/pith-number/H37ZYMZO4ARKHYRO4VOICG6JVL/graph.json","fetch_events":"https://pith.science/api/pith-number/H37ZYMZO4ARKHYRO4VOICG6JVL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H37ZYMZO4ARKHYRO4VOICG6JVL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H37ZYMZO4ARKHYRO4VOICG6JVL/action/storage_attestation","attest_author":"https://pith.science/pith/H37ZYMZO4ARKHYRO4VOICG6JVL/action/author_attestation","sign_citation":"https://pith.science/pith/H37ZYMZO4ARKHYRO4VOICG6JVL/action/citation_signature","submit_replication":"https://pith.science/pith/H37ZYMZO4ARKHYRO4VOICG6JVL/action/replication_record"}},"created_at":"2026-07-05T05:49:22.708826+00:00","updated_at":"2026-07-05T05:49:22.708826+00:00"}