{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:XQ7U7XPJUFHMWBUL4V4SQXZV76","short_pith_number":"pith:XQ7U7XPJ","canonical_record":{"source":{"id":"2504.14113","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-19T00:13:21Z","cross_cats_sorted":[],"title_canon_sha256":"be3b685cad1e2c7f7a4f4d87493d4312f7c6b6cee905ac1db29e3c1f36779ebe","abstract_canon_sha256":"59cc138f49c7310199e58fe3ae1782d21ed2d6ea648043de8f34600d8d6cf88c"},"schema_version":"1.0"},"canonical_sha256":"bc3f4fdde9a14ecb068be579285f35ffae39902011560c0f1d0c67c289f43338","source":{"kind":"arxiv","id":"2504.14113","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.14113","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"arxiv_version","alias_value":"2504.14113v1","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14113","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"pith_short_12","alias_value":"XQ7U7XPJUFHM","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"pith_short_16","alias_value":"XQ7U7XPJUFHMWBUL","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"pith_short_8","alias_value":"XQ7U7XPJ","created_at":"2026-07-05T10:51:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:XQ7U7XPJUFHMWBUL4V4SQXZV76","target":"record","payload":{"canonical_record":{"source":{"id":"2504.14113","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-19T00:13:21Z","cross_cats_sorted":[],"title_canon_sha256":"be3b685cad1e2c7f7a4f4d87493d4312f7c6b6cee905ac1db29e3c1f36779ebe","abstract_canon_sha256":"59cc138f49c7310199e58fe3ae1782d21ed2d6ea648043de8f34600d8d6cf88c"},"schema_version":"1.0"},"canonical_sha256":"bc3f4fdde9a14ecb068be579285f35ffae39902011560c0f1d0c67c289f43338","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:11.145263Z","signature_b64":"KXLNLeczUZjSG4LGbJVohCbM34zncE/L3p25wtd0cOxmebZ0xkhgJvWMqpX0VLYia5wlChbMd9NfSgqNoLghAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc3f4fdde9a14ecb068be579285f35ffae39902011560c0f1d0c67c289f43338","last_reissued_at":"2026-07-05T10:51:11.144713Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:11.144713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.14113","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":"2026-07-05T10:51:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZjKZEtPd0Qd3FzBlpFGsHVLvCeBR76E2lJGaMuj+jcp1QaU+tsO3z2btsMFATwItAbOtGN5Ns5XI4NBWesSIBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T15:09:37.325108Z"},"content_sha256":"af06369b481f573e3bd449a5daaa1f3a8091873aa359c4270db1b10ac77b943e","schema_version":"1.0","event_id":"sha256:af06369b481f573e3bd449a5daaa1f3a8091873aa359c4270db1b10ac77b943e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:XQ7U7XPJUFHMWBUL4V4SQXZV76","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Lightweight Road Environment Segmentation using Vector Quantization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alper Yilmaz, Charles Toth, Jiyong Kwag","submitted_at":"2025-04-19T00:13:21Z","abstract_excerpt":"Road environment segmentation plays a significant role in autonomous driving. Numerous works based on Fully Convolutional Networks (FCNs) and Transformer architectures have been proposed to leverage local and global contextual learning for efficient and accurate semantic segmentation. In both architectures, the encoder often relies heavily on extracting continuous representations from the image, which limits the ability to represent meaningful discrete information. To address this limitation, we propose segmentation of the autonomous driving environment using vector quantization. Vector quanti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14113","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/2504.14113/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":"2026-07-05T10:51:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MO4To17hjGnNcHmrNwY3CQhfxYsNz64Me+XoNmUQNS/AXRK57VKmyUPVmS7HteSCv2GJoGERIjQf2DgPO+awBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T15:09:37.325694Z"},"content_sha256":"efc7647abf727caf2850bf72ad4db686ab06cc4465b069084d0e44a9dd3a9c9a","schema_version":"1.0","event_id":"sha256:efc7647abf727caf2850bf72ad4db686ab06cc4465b069084d0e44a9dd3a9c9a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XQ7U7XPJUFHMWBUL4V4SQXZV76/bundle.json","state_url":"https://pith.science/pith/XQ7U7XPJUFHMWBUL4V4SQXZV76/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XQ7U7XPJUFHMWBUL4V4SQXZV76/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":"2026-08-23T15:09:37Z","links":{"resolver":"https://pith.science/pith/XQ7U7XPJUFHMWBUL4V4SQXZV76","bundle":"https://pith.science/pith/XQ7U7XPJUFHMWBUL4V4SQXZV76/bundle.json","state":"https://pith.science/pith/XQ7U7XPJUFHMWBUL4V4SQXZV76/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XQ7U7XPJUFHMWBUL4V4SQXZV76/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XQ7U7XPJUFHMWBUL4V4SQXZV76","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":"59cc138f49c7310199e58fe3ae1782d21ed2d6ea648043de8f34600d8d6cf88c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-19T00:13:21Z","title_canon_sha256":"be3b685cad1e2c7f7a4f4d87493d4312f7c6b6cee905ac1db29e3c1f36779ebe"},"schema_version":"1.0","source":{"id":"2504.14113","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.14113","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"arxiv_version","alias_value":"2504.14113v1","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14113","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"pith_short_12","alias_value":"XQ7U7XPJUFHM","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"pith_short_16","alias_value":"XQ7U7XPJUFHMWBUL","created_at":"2026-07-05T10:51:11Z"},{"alias_kind":"pith_short_8","alias_value":"XQ7U7XPJ","created_at":"2026-07-05T10:51:11Z"}],"graph_snapshots":[{"event_id":"sha256:efc7647abf727caf2850bf72ad4db686ab06cc4465b069084d0e44a9dd3a9c9a","target":"graph","created_at":"2026-07-05T10:51:11Z","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/2504.14113/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Road environment segmentation plays a significant role in autonomous driving. Numerous works based on Fully Convolutional Networks (FCNs) and Transformer architectures have been proposed to leverage local and global contextual learning for efficient and accurate semantic segmentation. In both architectures, the encoder often relies heavily on extracting continuous representations from the image, which limits the ability to represent meaningful discrete information. To address this limitation, we propose segmentation of the autonomous driving environment using vector quantization. Vector quanti","authors_text":"Alper Yilmaz, Charles Toth, Jiyong Kwag","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-19T00:13:21Z","title":"Lightweight Road Environment Segmentation using Vector Quantization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14113","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:af06369b481f573e3bd449a5daaa1f3a8091873aa359c4270db1b10ac77b943e","target":"record","created_at":"2026-07-05T10:51:11Z","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":"59cc138f49c7310199e58fe3ae1782d21ed2d6ea648043de8f34600d8d6cf88c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-19T00:13:21Z","title_canon_sha256":"be3b685cad1e2c7f7a4f4d87493d4312f7c6b6cee905ac1db29e3c1f36779ebe"},"schema_version":"1.0","source":{"id":"2504.14113","kind":"arxiv","version":1}},"canonical_sha256":"bc3f4fdde9a14ecb068be579285f35ffae39902011560c0f1d0c67c289f43338","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bc3f4fdde9a14ecb068be579285f35ffae39902011560c0f1d0c67c289f43338","first_computed_at":"2026-07-05T10:51:11.144713Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:51:11.144713Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KXLNLeczUZjSG4LGbJVohCbM34zncE/L3p25wtd0cOxmebZ0xkhgJvWMqpX0VLYia5wlChbMd9NfSgqNoLghAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:51:11.145263Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.14113","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:af06369b481f573e3bd449a5daaa1f3a8091873aa359c4270db1b10ac77b943e","sha256:efc7647abf727caf2850bf72ad4db686ab06cc4465b069084d0e44a9dd3a9c9a"],"state_sha256":"c753380d4e6230e8d504acdb3bba61f30305ea433262db64c76bc14cec26dfab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g6PBBpPiqtAUYzRtcE612YGHkoVXwsebgyredGHcdwkA4cl1d4tzOq2K+VVhSj8vqZJFF3tgQyxbRbniIW9eBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T15:09:37.330426Z","bundle_sha256":"ba27db123e14c28693af27f972cd53a8e54bf01451206f9b46a5f88c200657f2"}}