{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:N5PSJWWQWZFZJIATPTNPC4AOI3","short_pith_number":"pith:N5PSJWWQ","canonical_record":{"source":{"id":"1908.08652","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-23T03:30:53Z","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"title_canon_sha256":"3daeeccc4b9998116314a23fae5755beffeebbe0a58ffa4cb32d95ff0fad109e","abstract_canon_sha256":"d0dc503dc7229dd04a53a5b9b15da197b87b6b46b98ca4ad0551819adabc09b8"},"schema_version":"1.0"},"canonical_sha256":"6f5f24dad0b64b94a0137cdaf1700e46d7116a9eb5eff265fa5a2243002f84d7","source":{"kind":"arxiv","id":"1908.08652","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.08652","created_at":"2026-07-05T10:49:01Z"},{"alias_kind":"arxiv_version","alias_value":"1908.08652v1","created_at":"2026-07-05T10:49:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.08652","created_at":"2026-07-05T10:49:01Z"},{"alias_kind":"pith_short_12","alias_value":"N5PSJWWQWZFZ","created_at":"2026-07-05T10:49:01Z"},{"alias_kind":"pith_short_16","alias_value":"N5PSJWWQWZFZJIAT","created_at":"2026-07-05T10:49:01Z"},{"alias_kind":"pith_short_8","alias_value":"N5PSJWWQ","created_at":"2026-07-05T10:49:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:N5PSJWWQWZFZJIATPTNPC4AOI3","target":"record","payload":{"canonical_record":{"source":{"id":"1908.08652","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-23T03:30:53Z","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"title_canon_sha256":"3daeeccc4b9998116314a23fae5755beffeebbe0a58ffa4cb32d95ff0fad109e","abstract_canon_sha256":"d0dc503dc7229dd04a53a5b9b15da197b87b6b46b98ca4ad0551819adabc09b8"},"schema_version":"1.0"},"canonical_sha256":"6f5f24dad0b64b94a0137cdaf1700e46d7116a9eb5eff265fa5a2243002f84d7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:49:01.307624Z","signature_b64":"+Hv8bvA+1joKIyqTs8U7y1fpNDLSQ6xuHSqC95swiB4S6MTPNRKp++llVJ3NTgeDytSR6ihPIWAHgNBbmeIQAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f5f24dad0b64b94a0137cdaf1700e46d7116a9eb5eff265fa5a2243002f84d7","last_reissued_at":"2026-07-05T10:49:01.307176Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:49:01.307176Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.08652","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":"2026-07-05T10:49:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3YASpikI3pX1AQsHrjovBx8SfhEd6UEu+s8xkk9YTd3Dt+1fZYsAIXFskeiIfws8PKJlAmr5auSe+hKiuaOJCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T18:33:38.810622Z"},"content_sha256":"8b98e1713c47da58c62502f27ffbb3f7d06685dc73f5b8996e85f2c3a15ca719","schema_version":"1.0","event_id":"sha256:8b98e1713c47da58c62502f27ffbb3f7d06685dc73f5b8996e85f2c3a15ca719"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:N5PSJWWQWZFZJIATPTNPC4AOI3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Abhay Kumar, Chirag Singh, Kamal Krishna, Nishant Jain, Suraj Tripathi","submitted_at":"2019-08-23T03:30:53Z","abstract_excerpt":"We propose a Multi-Task Learning (MTL) paradigm based deep neural network architecture, called MTCNet (Multi-Task Crowd Network) for crowd density and count estimation. Crowd count estimation is challenging due to the non-uniform scale variations and the arbitrary perspective of an individual image. The proposed model has two related tasks, with Crowd Density Estimation as the main task and Crowd-Count Group Classification as the auxiliary task. The auxiliary task helps in capturing the relevant scale-related information to improve the performance of the main task. The main task model comprise"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.08652","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/1908.08652/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:49:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zVfW5DPxF2nXiCvdxR9TTBzcQK8dpn+scPbfoD8/ulneyp3ygvqyzKvIKfDrp3PUDoUpJTw43HzIIkD5oj8jAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T18:33:38.811591Z"},"content_sha256":"4a9c6f68762c5d3f11876f3604c5312d20fd560a0433d9ede0d89e994885ab6a","schema_version":"1.0","event_id":"sha256:4a9c6f68762c5d3f11876f3604c5312d20fd560a0433d9ede0d89e994885ab6a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N5PSJWWQWZFZJIATPTNPC4AOI3/bundle.json","state_url":"https://pith.science/pith/N5PSJWWQWZFZJIATPTNPC4AOI3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N5PSJWWQWZFZJIATPTNPC4AOI3/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":"2026-08-16T18:33:38Z","links":{"resolver":"https://pith.science/pith/N5PSJWWQWZFZJIATPTNPC4AOI3","bundle":"https://pith.science/pith/N5PSJWWQWZFZJIATPTNPC4AOI3/bundle.json","state":"https://pith.science/pith/N5PSJWWQWZFZJIATPTNPC4AOI3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N5PSJWWQWZFZJIATPTNPC4AOI3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:N5PSJWWQWZFZJIATPTNPC4AOI3","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":"d0dc503dc7229dd04a53a5b9b15da197b87b6b46b98ca4ad0551819adabc09b8","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-23T03:30:53Z","title_canon_sha256":"3daeeccc4b9998116314a23fae5755beffeebbe0a58ffa4cb32d95ff0fad109e"},"schema_version":"1.0","source":{"id":"1908.08652","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.08652","created_at":"2026-07-05T10:49:01Z"},{"alias_kind":"arxiv_version","alias_value":"1908.08652v1","created_at":"2026-07-05T10:49:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.08652","created_at":"2026-07-05T10:49:01Z"},{"alias_kind":"pith_short_12","alias_value":"N5PSJWWQWZFZ","created_at":"2026-07-05T10:49:01Z"},{"alias_kind":"pith_short_16","alias_value":"N5PSJWWQWZFZJIAT","created_at":"2026-07-05T10:49:01Z"},{"alias_kind":"pith_short_8","alias_value":"N5PSJWWQ","created_at":"2026-07-05T10:49:01Z"}],"graph_snapshots":[{"event_id":"sha256:4a9c6f68762c5d3f11876f3604c5312d20fd560a0433d9ede0d89e994885ab6a","target":"graph","created_at":"2026-07-05T10:49:01Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1908.08652/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a Multi-Task Learning (MTL) paradigm based deep neural network architecture, called MTCNet (Multi-Task Crowd Network) for crowd density and count estimation. Crowd count estimation is challenging due to the non-uniform scale variations and the arbitrary perspective of an individual image. The proposed model has two related tasks, with Crowd Density Estimation as the main task and Crowd-Count Group Classification as the auxiliary task. The auxiliary task helps in capturing the relevant scale-related information to improve the performance of the main task. The main task model comprise","authors_text":"Abhay Kumar, Chirag Singh, Kamal Krishna, Nishant Jain, Suraj Tripathi","cross_cats":["cs.AI","cs.CV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-23T03:30:53Z","title":"MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.08652","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":"sha256:8b98e1713c47da58c62502f27ffbb3f7d06685dc73f5b8996e85f2c3a15ca719","target":"record","created_at":"2026-07-05T10:49:01Z","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":"d0dc503dc7229dd04a53a5b9b15da197b87b6b46b98ca4ad0551819adabc09b8","cross_cats_sorted":["cs.AI","cs.CV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-23T03:30:53Z","title_canon_sha256":"3daeeccc4b9998116314a23fae5755beffeebbe0a58ffa4cb32d95ff0fad109e"},"schema_version":"1.0","source":{"id":"1908.08652","kind":"arxiv","version":1}},"canonical_sha256":"6f5f24dad0b64b94a0137cdaf1700e46d7116a9eb5eff265fa5a2243002f84d7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6f5f24dad0b64b94a0137cdaf1700e46d7116a9eb5eff265fa5a2243002f84d7","first_computed_at":"2026-07-05T10:49:01.307176Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:49:01.307176Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+Hv8bvA+1joKIyqTs8U7y1fpNDLSQ6xuHSqC95swiB4S6MTPNRKp++llVJ3NTgeDytSR6ihPIWAHgNBbmeIQAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:49:01.307624Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.08652","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8b98e1713c47da58c62502f27ffbb3f7d06685dc73f5b8996e85f2c3a15ca719","sha256:4a9c6f68762c5d3f11876f3604c5312d20fd560a0433d9ede0d89e994885ab6a"],"state_sha256":"a97a8d5dd5805c991a52189f97b70e906c7fb843689d1cd1d01d547486bdf689"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XQnOMTN/QL3Lvvbm9n/FGNdnEIWqw4uH+tKoQJ5E1R8dtkZT4RTd8y+E5/n8qgkYeFHzy0Q3KqlHFL1jSSt3Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T18:33:38.819698Z","bundle_sha256":"eccddf022253f630883f7ac9f4d31b6e635ebd24ac8e33b2e8b3aa05065d05ce"}}