{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:U3YKKT6R4B34FZMAMSPGLFJPN3","short_pith_number":"pith:U3YKKT6R","schema_version":"1.0","canonical_sha256":"a6f0a54fd1e077c2e580649e65952f6ec23e9c6c43d8d9ae2aab0e2d1405e4e5","source":{"kind":"arxiv","id":"2205.15955","version":1},"attestation_state":"computed","paper":{"title":"CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Hongdong Li, Ian Reid, Junlin Han, Lars Petersson","submitted_at":"2022-05-31T16:57:28Z","abstract_excerpt":"We present a simple method, CropMix, for the purpose of producing a rich input distribution from the original dataset distribution. Unlike single random cropping, which may inadvertently capture only limited information, or irrelevant information, like pure background, unrelated objects, etc, we crop an image multiple times using distinct crop scales, thereby ensuring that multi-scale information is captured. The new input distribution, serving as training data, useful for a number of vision tasks, is then formed by simply mixing multiple cropped views. We first demonstrate that CropMix can be"},"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":"2205.15955","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-05-31T16:57:28Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"d343946c840b20b3101190f6d3996a024e364eb09bc0c7a589708f4a4bf1b2e1","abstract_canon_sha256":"7c7b4b50f5dbfe23a5e6da7cee9d360cd916cc0454356cb163b999db0065a907"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:28:04.216521Z","signature_b64":"efC1Mef9r//6v1KVSoNBtOa6LTY17BXbsf8C58lVBxVGiR4E3CCpb7xNOcN7ARQw/Xzj+MGFHHb19378wgFXBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6f0a54fd1e077c2e580649e65952f6ec23e9c6c43d8d9ae2aab0e2d1405e4e5","last_reissued_at":"2026-07-05T04:28:04.216025Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:28:04.216025Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Hongdong Li, Ian Reid, Junlin Han, Lars Petersson","submitted_at":"2022-05-31T16:57:28Z","abstract_excerpt":"We present a simple method, CropMix, for the purpose of producing a rich input distribution from the original dataset distribution. Unlike single random cropping, which may inadvertently capture only limited information, or irrelevant information, like pure background, unrelated objects, etc, we crop an image multiple times using distinct crop scales, thereby ensuring that multi-scale information is captured. The new input distribution, serving as training data, useful for a number of vision tasks, is then formed by simply mixing multiple cropped views. We first demonstrate that CropMix can be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.15955","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/2205.15955/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":"2205.15955","created_at":"2026-07-05T04:28:04.216085+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.15955v1","created_at":"2026-07-05T04:28:04.216085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.15955","created_at":"2026-07-05T04:28:04.216085+00:00"},{"alias_kind":"pith_short_12","alias_value":"U3YKKT6R4B34","created_at":"2026-07-05T04:28:04.216085+00:00"},{"alias_kind":"pith_short_16","alias_value":"U3YKKT6R4B34FZMA","created_at":"2026-07-05T04:28:04.216085+00:00"},{"alias_kind":"pith_short_8","alias_value":"U3YKKT6R","created_at":"2026-07-05T04:28:04.216085+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25784","citing_title":"$S^{2}$-FracMix: Label-Preserving Self-Saliency Mixup Augmentation","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2212.02011","citing_title":"PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning","ref_index":55,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U3YKKT6R4B34FZMAMSPGLFJPN3","json":"https://pith.science/pith/U3YKKT6R4B34FZMAMSPGLFJPN3.json","graph_json":"https://pith.science/api/pith-number/U3YKKT6R4B34FZMAMSPGLFJPN3/graph.json","events_json":"https://pith.science/api/pith-number/U3YKKT6R4B34FZMAMSPGLFJPN3/events.json","paper":"https://pith.science/paper/U3YKKT6R"},"agent_actions":{"view_html":"https://pith.science/pith/U3YKKT6R4B34FZMAMSPGLFJPN3","download_json":"https://pith.science/pith/U3YKKT6R4B34FZMAMSPGLFJPN3.json","view_paper":"https://pith.science/paper/U3YKKT6R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.15955&json=true","fetch_graph":"https://pith.science/api/pith-number/U3YKKT6R4B34FZMAMSPGLFJPN3/graph.json","fetch_events":"https://pith.science/api/pith-number/U3YKKT6R4B34FZMAMSPGLFJPN3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U3YKKT6R4B34FZMAMSPGLFJPN3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U3YKKT6R4B34FZMAMSPGLFJPN3/action/storage_attestation","attest_author":"https://pith.science/pith/U3YKKT6R4B34FZMAMSPGLFJPN3/action/author_attestation","sign_citation":"https://pith.science/pith/U3YKKT6R4B34FZMAMSPGLFJPN3/action/citation_signature","submit_replication":"https://pith.science/pith/U3YKKT6R4B34FZMAMSPGLFJPN3/action/replication_record"}},"created_at":"2026-07-05T04:28:04.216085+00:00","updated_at":"2026-07-05T04:28:04.216085+00:00"}