{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:IPMTHNENH3RMYTMAMOLYLRVIXD","short_pith_number":"pith:IPMTHNEN","canonical_record":{"source":{"id":"1807.09993","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T07:52:17Z","cross_cats_sorted":[],"title_canon_sha256":"e1dbeb52269d2f111717c9c1cec89977c00923c2a59b58e6653a559e2da080db","abstract_canon_sha256":"526de34d0df524bec1ae465f6cc0a70893c6580939016c86c7bc481d1b162dae"},"schema_version":"1.0"},"canonical_sha256":"43d933b48d3ee2cc4d80639785c6a8b8dca3d4c1e1455a236c1d4cdca04c5d51","source":{"kind":"arxiv","id":"1807.09993","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.09993","created_at":"2026-05-18T00:09:45Z"},{"alias_kind":"arxiv_version","alias_value":"1807.09993v1","created_at":"2026-05-18T00:09:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.09993","created_at":"2026-05-18T00:09:45Z"},{"alias_kind":"pith_short_12","alias_value":"IPMTHNENH3RM","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IPMTHNENH3RMYTMA","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IPMTHNEN","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:IPMTHNENH3RMYTMAMOLYLRVIXD","target":"record","payload":{"canonical_record":{"source":{"id":"1807.09993","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T07:52:17Z","cross_cats_sorted":[],"title_canon_sha256":"e1dbeb52269d2f111717c9c1cec89977c00923c2a59b58e6653a559e2da080db","abstract_canon_sha256":"526de34d0df524bec1ae465f6cc0a70893c6580939016c86c7bc481d1b162dae"},"schema_version":"1.0"},"canonical_sha256":"43d933b48d3ee2cc4d80639785c6a8b8dca3d4c1e1455a236c1d4cdca04c5d51","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:45.783294Z","signature_b64":"08LTF67dp9TlKSnjoUdwAXat820tIHCx/UYQRIyq67CIrgI4IMDjOYmQw5qOAkd/fuvFN/qxCsfNWRyhajTWAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43d933b48d3ee2cc4d80639785c6a8b8dca3d4c1e1455a236c1d4cdca04c5d51","last_reissued_at":"2026-05-18T00:09:45.782869Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:45.782869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.09993","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:09:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CXOoMnzPGg5A388A01RLzOdb43ngEuhp+HGvB3hUKwBkBLax11KOQ8vUXYvh5Ym1e8c6ji8aQ3KvEijFPw0RDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T06:19:44.410889Z"},"content_sha256":"27ae24fddc08e1568688cfe37ed2d46f3a39f03f44f29a922fedbf4a512fe2ab","schema_version":"1.0","event_id":"sha256:27ae24fddc08e1568688cfe37ed2d46f3a39f03f44f29a922fedbf4a512fe2ab"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:IPMTHNENH3RMYTMAMOLYLRVIXD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Deepak Babu Sam, Neeraj N Sajjan, R. Venkatesh Babu","submitted_at":"2018-07-26T07:52:17Z","abstract_excerpt":"Automated counting of people in crowd images is a challenging task. The major difficulty stems from the large diversity in the way people appear in crowds. In fact, features available for crowd discrimination largely depend on the crowd density to the extent that people are only seen as blobs in a highly dense scene. We tackle this problem with a growing CNN which can progressively increase its capacity to account for the wide variability seen in crowd scenes. Our model starts from a base CNN density regressor, which is trained in equivalence on all types of crowd images. In order to adapt wit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.09993","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:09:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JahPG8lJ6frpTWb0QnvhIPhiNjJtT9PgUl28CYuFyPCI1GM/mUvqIfs14s0S/0muobGNdFABAga7LN4X5hkuCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T06:19:44.411228Z"},"content_sha256":"a6cc69171721e836987efa70c4de30c37a0c846ca26900ebf4c690c4f162ab72","schema_version":"1.0","event_id":"sha256:a6cc69171721e836987efa70c4de30c37a0c846ca26900ebf4c690c4f162ab72"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IPMTHNENH3RMYTMAMOLYLRVIXD/bundle.json","state_url":"https://pith.science/pith/IPMTHNENH3RMYTMAMOLYLRVIXD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IPMTHNENH3RMYTMAMOLYLRVIXD/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-03T06:19:44Z","links":{"resolver":"https://pith.science/pith/IPMTHNENH3RMYTMAMOLYLRVIXD","bundle":"https://pith.science/pith/IPMTHNENH3RMYTMAMOLYLRVIXD/bundle.json","state":"https://pith.science/pith/IPMTHNENH3RMYTMAMOLYLRVIXD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IPMTHNENH3RMYTMAMOLYLRVIXD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:IPMTHNENH3RMYTMAMOLYLRVIXD","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"526de34d0df524bec1ae465f6cc0a70893c6580939016c86c7bc481d1b162dae","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T07:52:17Z","title_canon_sha256":"e1dbeb52269d2f111717c9c1cec89977c00923c2a59b58e6653a559e2da080db"},"schema_version":"1.0","source":{"id":"1807.09993","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.09993","created_at":"2026-05-18T00:09:45Z"},{"alias_kind":"arxiv_version","alias_value":"1807.09993v1","created_at":"2026-05-18T00:09:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.09993","created_at":"2026-05-18T00:09:45Z"},{"alias_kind":"pith_short_12","alias_value":"IPMTHNENH3RM","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IPMTHNENH3RMYTMA","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IPMTHNEN","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:a6cc69171721e836987efa70c4de30c37a0c846ca26900ebf4c690c4f162ab72","target":"graph","created_at":"2026-05-18T00:09:45Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Automated counting of people in crowd images is a challenging task. The major difficulty stems from the large diversity in the way people appear in crowds. In fact, features available for crowd discrimination largely depend on the crowd density to the extent that people are only seen as blobs in a highly dense scene. We tackle this problem with a growing CNN which can progressively increase its capacity to account for the wide variability seen in crowd scenes. Our model starts from a base CNN density regressor, which is trained in equivalence on all types of crowd images. In order to adapt wit","authors_text":"Deepak Babu Sam, Neeraj N Sajjan, R. Venkatesh Babu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T07:52:17Z","title":"Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.09993","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:27ae24fddc08e1568688cfe37ed2d46f3a39f03f44f29a922fedbf4a512fe2ab","target":"record","created_at":"2026-05-18T00:09:45Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"526de34d0df524bec1ae465f6cc0a70893c6580939016c86c7bc481d1b162dae","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-26T07:52:17Z","title_canon_sha256":"e1dbeb52269d2f111717c9c1cec89977c00923c2a59b58e6653a559e2da080db"},"schema_version":"1.0","source":{"id":"1807.09993","kind":"arxiv","version":1}},"canonical_sha256":"43d933b48d3ee2cc4d80639785c6a8b8dca3d4c1e1455a236c1d4cdca04c5d51","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43d933b48d3ee2cc4d80639785c6a8b8dca3d4c1e1455a236c1d4cdca04c5d51","first_computed_at":"2026-05-18T00:09:45.782869Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:09:45.782869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"08LTF67dp9TlKSnjoUdwAXat820tIHCx/UYQRIyq67CIrgI4IMDjOYmQw5qOAkd/fuvFN/qxCsfNWRyhajTWAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:09:45.783294Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.09993","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:27ae24fddc08e1568688cfe37ed2d46f3a39f03f44f29a922fedbf4a512fe2ab","sha256:a6cc69171721e836987efa70c4de30c37a0c846ca26900ebf4c690c4f162ab72"],"state_sha256":"2b3ae20d4e9748575300e8c5450953358784d955f9d2893e321348413a71a3d9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lSku70TjqJkac4AaBywG135USO5Sg0TOIvzylrMxhAwe4B9g5g4zJbF3NhAD4tITBtvUt590UJRlK6eW06qkCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T06:19:44.413076Z","bundle_sha256":"72f06eddb8e49eda9543d4e8d18fdf3215c7649e64cbd7f60bc298c84e15432d"}}