{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YVPKOE6C22JE5D6VFOYOMOV6PB","short_pith_number":"pith:YVPKOE6C","schema_version":"1.0","canonical_sha256":"c55ea713c2d6924e8fd52bb0e63abe786465114017f7c0a9ac931d2c96414b9e","source":{"kind":"arxiv","id":"2607.23680","version":1},"attestation_state":"computed","paper":{"title":"Perturbation-Aware Diffusion-Guided Hybrid Segmentation for Robust and Annotation-Efficient Plant Stress Phenotyping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gurbhit Chaurakoti, Soumyashree Kar","submitted_at":"2026-07-26T14:28:05Z","abstract_excerpt":"Semantic segmentation in agricultural imagery is often evaluated under in-domain protocols, yet practical deployment requires robustness to appearance perturbations, limited annotations, and cross domain shift. This paper presents a diffusion-guided hybrid segmentation framework in which U-Net, DeepLabV3+, and SegFormer backbones generate coarse masks that are refined by Denoising Diffusion Probabilistic Models (DDPM), latent diffusion, or semantic-guided diffusion. The framework is evaluated through a 3x3 architectural screening study on PlantSegV3, followed by boundary-constrained optimizati"},"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":"2607.23680","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-26T14:28:05Z","cross_cats_sorted":[],"title_canon_sha256":"6f2be18be630584dbe50d89292d9dfc6387e1212349257b7707e99bbae261c1a","abstract_canon_sha256":"1a26ba052793a1570f07c79ec28e2a5b5d706df4d8047efcb0ab9f57b43bb51a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T01:23:05.098357Z","signature_b64":"TRZV0UckBjKfI2MjekMtzbRZ0i7abKonJZVXOliaw8owLVyKTA+GmdLriqwhTgIBeCjb4HXhI2LlNC2SHV/7Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c55ea713c2d6924e8fd52bb0e63abe786465114017f7c0a9ac931d2c96414b9e","last_reissued_at":"2026-07-28T01:23:05.097537Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T01:23:05.097537Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Perturbation-Aware Diffusion-Guided Hybrid Segmentation for Robust and Annotation-Efficient Plant Stress Phenotyping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gurbhit Chaurakoti, Soumyashree Kar","submitted_at":"2026-07-26T14:28:05Z","abstract_excerpt":"Semantic segmentation in agricultural imagery is often evaluated under in-domain protocols, yet practical deployment requires robustness to appearance perturbations, limited annotations, and cross domain shift. This paper presents a diffusion-guided hybrid segmentation framework in which U-Net, DeepLabV3+, and SegFormer backbones generate coarse masks that are refined by Denoising Diffusion Probabilistic Models (DDPM), latent diffusion, or semantic-guided diffusion. The framework is evaluated through a 3x3 architectural screening study on PlantSegV3, followed by boundary-constrained optimizati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.23680","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/2607.23680/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":"2607.23680","created_at":"2026-07-28T01:23:05.097957+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.23680v1","created_at":"2026-07-28T01:23:05.097957+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.23680","created_at":"2026-07-28T01:23:05.097957+00:00"},{"alias_kind":"pith_short_12","alias_value":"YVPKOE6C22JE","created_at":"2026-07-28T01:23:05.097957+00:00"},{"alias_kind":"pith_short_16","alias_value":"YVPKOE6C22JE5D6V","created_at":"2026-07-28T01:23:05.097957+00:00"},{"alias_kind":"pith_short_8","alias_value":"YVPKOE6C","created_at":"2026-07-28T01:23:05.097957+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/YVPKOE6C22JE5D6VFOYOMOV6PB","json":"https://pith.science/pith/YVPKOE6C22JE5D6VFOYOMOV6PB.json","graph_json":"https://pith.science/api/pith-number/YVPKOE6C22JE5D6VFOYOMOV6PB/graph.json","events_json":"https://pith.science/api/pith-number/YVPKOE6C22JE5D6VFOYOMOV6PB/events.json","paper":"https://pith.science/paper/YVPKOE6C"},"agent_actions":{"view_html":"https://pith.science/pith/YVPKOE6C22JE5D6VFOYOMOV6PB","download_json":"https://pith.science/pith/YVPKOE6C22JE5D6VFOYOMOV6PB.json","view_paper":"https://pith.science/paper/YVPKOE6C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.23680&json=true","fetch_graph":"https://pith.science/api/pith-number/YVPKOE6C22JE5D6VFOYOMOV6PB/graph.json","fetch_events":"https://pith.science/api/pith-number/YVPKOE6C22JE5D6VFOYOMOV6PB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YVPKOE6C22JE5D6VFOYOMOV6PB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YVPKOE6C22JE5D6VFOYOMOV6PB/action/storage_attestation","attest_author":"https://pith.science/pith/YVPKOE6C22JE5D6VFOYOMOV6PB/action/author_attestation","sign_citation":"https://pith.science/pith/YVPKOE6C22JE5D6VFOYOMOV6PB/action/citation_signature","submit_replication":"https://pith.science/pith/YVPKOE6C22JE5D6VFOYOMOV6PB/action/replication_record"}},"created_at":"2026-07-28T01:23:05.097957+00:00","updated_at":"2026-07-28T01:23:05.097957+00:00"}