{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:QYEFJXBTQAEN7M6LSPLGZ3JFBR","short_pith_number":"pith:QYEFJXBT","canonical_record":{"source":{"id":"2211.05778","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-11-10T18:59:04Z","cross_cats_sorted":[],"title_canon_sha256":"09c80f168e587fd63382cf741c6f70b69b39d6e0145f8caaf43edefe383433eb","abstract_canon_sha256":"60a6b5461580de45ec5c7c07f021052d257a79d7ff9bb628f622e70ad5d7cfc6"},"schema_version":"1.0"},"canonical_sha256":"860854dc338008dfb3cb93d66ced250c7c6345f70ea7846800ae3b129f67b4ac","source":{"kind":"arxiv","id":"2211.05778","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.05778","created_at":"2026-07-05T06:01:35Z"},{"alias_kind":"arxiv_version","alias_value":"2211.05778v4","created_at":"2026-07-05T06:01:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.05778","created_at":"2026-07-05T06:01:35Z"},{"alias_kind":"pith_short_12","alias_value":"QYEFJXBTQAEN","created_at":"2026-07-05T06:01:35Z"},{"alias_kind":"pith_short_16","alias_value":"QYEFJXBTQAEN7M6L","created_at":"2026-07-05T06:01:35Z"},{"alias_kind":"pith_short_8","alias_value":"QYEFJXBT","created_at":"2026-07-05T06:01:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:QYEFJXBTQAEN7M6LSPLGZ3JFBR","target":"record","payload":{"canonical_record":{"source":{"id":"2211.05778","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-11-10T18:59:04Z","cross_cats_sorted":[],"title_canon_sha256":"09c80f168e587fd63382cf741c6f70b69b39d6e0145f8caaf43edefe383433eb","abstract_canon_sha256":"60a6b5461580de45ec5c7c07f021052d257a79d7ff9bb628f622e70ad5d7cfc6"},"schema_version":"1.0"},"canonical_sha256":"860854dc338008dfb3cb93d66ced250c7c6345f70ea7846800ae3b129f67b4ac","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:01:35.927274Z","signature_b64":"YAKOnUHyeVngS3xcDTSLHUERmCISRGivI7DOlQiaZiNYt6Rl2fTdrr/OcnPqPl37ly+MbHGJ31bdsXfI93xhDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"860854dc338008dfb3cb93d66ced250c7c6345f70ea7846800ae3b129f67b4ac","last_reissued_at":"2026-07-05T06:01:35.926856Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:01:35.926856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.05778","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:01:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"quLaR7OCyb3MJWRDwiwPKogP1L/YPbdRN0N6KWXnCsOA1nCYwbbg3NJkq9v37rNoAZBYEsvgqP86Fcq5rgrdDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T21:06:52.022743Z"},"content_sha256":"f1d6eb1b27fbb8b40f90f4ce6c316abdc1e7e049ed3b349cb49f9e9fc59eb129","schema_version":"1.0","event_id":"sha256:f1d6eb1b27fbb8b40f90f4ce6c316abdc1e7e049ed3b349cb49f9e9fc59eb129"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:QYEFJXBTQAEN7M6LSPLGZ3JFBR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongsheng Li, Jifeng Dai, Lewei Lu, Tong Lu, Wenhai Wang, Xiaogang Wang, Xiaowei Hu, Xizhou Zhu, Yu Qiao, Zhe Chen, Zhenhang Huang, Zhiqi Li","submitted_at":"2022-11-10T18:59:04Z","abstract_excerpt":"Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.05778","kind":"arxiv","version":4},"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/2211.05778/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":"2026-07-05T06:01:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kElvKql28kj0f/eL6oXX9/LPLO/lxH361HBPlR8cjr/4VbbVMyD8f1SHa35yA1885y4ntqeXTbSvPoq726tiAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T21:06:52.023244Z"},"content_sha256":"326878e7564fc6eceed1c707fd616a64bf85f760e6cc81ca3a443fe19566176c","schema_version":"1.0","event_id":"sha256:326878e7564fc6eceed1c707fd616a64bf85f760e6cc81ca3a443fe19566176c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QYEFJXBTQAEN7M6LSPLGZ3JFBR/bundle.json","state_url":"https://pith.science/pith/QYEFJXBTQAEN7M6LSPLGZ3JFBR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QYEFJXBTQAEN7M6LSPLGZ3JFBR/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":"2026-08-10T21:06:52Z","links":{"resolver":"https://pith.science/pith/QYEFJXBTQAEN7M6LSPLGZ3JFBR","bundle":"https://pith.science/pith/QYEFJXBTQAEN7M6LSPLGZ3JFBR/bundle.json","state":"https://pith.science/pith/QYEFJXBTQAEN7M6LSPLGZ3JFBR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QYEFJXBTQAEN7M6LSPLGZ3JFBR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:QYEFJXBTQAEN7M6LSPLGZ3JFBR","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":"60a6b5461580de45ec5c7c07f021052d257a79d7ff9bb628f622e70ad5d7cfc6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-11-10T18:59:04Z","title_canon_sha256":"09c80f168e587fd63382cf741c6f70b69b39d6e0145f8caaf43edefe383433eb"},"schema_version":"1.0","source":{"id":"2211.05778","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.05778","created_at":"2026-07-05T06:01:35Z"},{"alias_kind":"arxiv_version","alias_value":"2211.05778v4","created_at":"2026-07-05T06:01:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.05778","created_at":"2026-07-05T06:01:35Z"},{"alias_kind":"pith_short_12","alias_value":"QYEFJXBTQAEN","created_at":"2026-07-05T06:01:35Z"},{"alias_kind":"pith_short_16","alias_value":"QYEFJXBTQAEN7M6L","created_at":"2026-07-05T06:01:35Z"},{"alias_kind":"pith_short_8","alias_value":"QYEFJXBT","created_at":"2026-07-05T06:01:35Z"}],"graph_snapshots":[{"event_id":"sha256:326878e7564fc6eceed1c707fd616a64bf85f760e6cc81ca3a443fe19566176c","target":"graph","created_at":"2026-07-05T06:01:35Z","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/2211.05778/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and s","authors_text":"Hongsheng Li, Jifeng Dai, Lewei Lu, Tong Lu, Wenhai Wang, Xiaogang Wang, Xiaowei Hu, Xizhou Zhu, Yu Qiao, Zhe Chen, Zhenhang Huang, Zhiqi Li","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-11-10T18:59:04Z","title":"InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.05778","kind":"arxiv","version":4},"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:f1d6eb1b27fbb8b40f90f4ce6c316abdc1e7e049ed3b349cb49f9e9fc59eb129","target":"record","created_at":"2026-07-05T06:01:35Z","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":"60a6b5461580de45ec5c7c07f021052d257a79d7ff9bb628f622e70ad5d7cfc6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-11-10T18:59:04Z","title_canon_sha256":"09c80f168e587fd63382cf741c6f70b69b39d6e0145f8caaf43edefe383433eb"},"schema_version":"1.0","source":{"id":"2211.05778","kind":"arxiv","version":4}},"canonical_sha256":"860854dc338008dfb3cb93d66ced250c7c6345f70ea7846800ae3b129f67b4ac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"860854dc338008dfb3cb93d66ced250c7c6345f70ea7846800ae3b129f67b4ac","first_computed_at":"2026-07-05T06:01:35.926856Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:01:35.926856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YAKOnUHyeVngS3xcDTSLHUERmCISRGivI7DOlQiaZiNYt6Rl2fTdrr/OcnPqPl37ly+MbHGJ31bdsXfI93xhDw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:01:35.927274Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.05778","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f1d6eb1b27fbb8b40f90f4ce6c316abdc1e7e049ed3b349cb49f9e9fc59eb129","sha256:326878e7564fc6eceed1c707fd616a64bf85f760e6cc81ca3a443fe19566176c"],"state_sha256":"7a5d5e1ae5f4499dc6893c5fa2a28e64de5837303d554b2cd9110e3e307553ff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uSEIOhPCOsLXRMXOEQa3t222a9Cl95AJKEaEnKJUZNmJQSAv0KhY4XtQTVHhvQNr15BmS5UE2f4rGiWZM6M+CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T21:06:52.028732Z","bundle_sha256":"0317cb54520d739e6383a248d4ac665d3ab543b79a9e88f52161cb7e20d2c7d9"}}