Paper Citation Record · LEDGER

Global and Local Entailment Learning for Natural World Imagery

As of 9 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2506.21476.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.21476 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:33:35.505115Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-09T20:05:21.069489Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T15:26:08.149245Z

Reference resolution

48 of 48 outbound references displayed

verified exact4
verified fuzzy32
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 83d1a7ed-358b-499d-a14e-9d2830a8d932 · outbound

This paper cites Emergent visual- semantic hierarchies in image-text representations.

Global and Local Entailment Learning for Natural World Imagery Emergent visual- semantic hierarchies in image-text representations

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:42.074702Z

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Observation 8ae2afdb-cce2-43ea-950b-cae969c0e5a2 · outbound

This paper cites Multi-relational poincar ´e graph embeddings.

Global and Local Entailment Learning for Natural World Imagery Multi-relational poincar ´e graph embeddings

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:41.853157Z

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Observation ee9f7430-dd7d-480f-861f-47bd17da1e59 · outbound

This paper cites Recognition in terra incognita.

Global and Local Entailment Learning for Natural World Imagery Recognition in terra incognita

Reference 3

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Observation 2665e193-9a78-40a3-9f8f-aad944215b79 · outbound

This paper cites Hyperbolic graph convolutional neural networks.

Global and Local Entailment Learning for Natural World Imagery Hyperbolic graph convolutional neural networks

Reference 4

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Observation ac751a6b-0092-4bfd-a377-90e1c4a98dad · outbound

This paper cites Low-Dimensional Hyperbolic Knowledge Graph Embeddings.

Global and Local Entailment Learning for Natural World Imagery Low-Dimensional Hyperbolic Knowledge Graph Embeddings

Reference 5

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Observation fabb0f11-7468-4714-9555-2690c1eb246f · outbound

This paper cites Mammalnet: A large-scale video benchmark for mammal recognition and behavior understanding.

Global and Local Entailment Learning for Natural World Imagery Mammalnet: A large-scale video benchmark for mammal recognition and behavior understanding

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:41.456713Z

Source-reported events for the cited work

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Observation 0f202ed4-f5c8-4568-b639-3dfe9e5f4f48 · outbound

This paper cites Mitree: Multi-input transformer ecoregion encoder for species distribution mod- elling.

Global and Local Entailment Learning for Natural World Imagery Mitree: Multi-input transformer ecoregion encoder for species distribution mod- elling

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:41.272540Z

Source-reported events for the cited work

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Observation 0754a917-d5c5-4484-9e9b-6bc05772ec56 · outbound

This paper cites Revis- iting multimodal representation in contrastive learning: from patch and token embeddings to finite discrete tokens.

Global and Local Entailment Learning for Natural World Imagery Revis- iting multimodal representation in contrastive learning: from patch and token embeddings to finite discrete tokens

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:41.067117Z

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Observation 69470baf-af21-4f91-aa57-05ef544c51e9 · outbound

This paper cites Probabilistic language-image pre-training.

Global and Local Entailment Learning for Natural World Imagery Probabilistic language-image pre-training

Reference 9

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Observation b9f2e2c1-4a3b-4234-8764-265678d466c2 · outbound

This paper cites WildSAT: Learning Satellite Image Representations from Wildlife Observations.

Global and Local Entailment Learning for Natural World Imagery WildSAT: Learning Satellite Image Representations from Wildlife Observations

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:33:36.226591Z

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Observation 01cc59c7-8dfa-4f5e-abc3-4d4d9baa9e18 · outbound

This paper cites Hyper- bolic image-text representations.

Global and Local Entailment Learning for Natural World Imagery Hyper- bolic image-text representations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:40.943341Z

Source-reported events for the cited work

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Observation 036ef8f8-5788-4bd1-bced-376e0246b2cf · outbound

This paper cites Embedding text in hyperbolic spaces.

Global and Local Entailment Learning for Natural World Imagery Embedding text in hyperbolic spaces

Reference 12

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Observation b3183e52-f2d8-4066-a7db-dd54e96658c6 · outbound

This paper cites Logics for approximate and strong en- tailments.

Global and Local Entailment Learning for Natural World Imagery Logics for approximate and strong en- tailments

Reference 13

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Source-reported events for the cited work

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Observation 0eabdd7e-51fa-4088-b6e8-2e7a06941f9f · outbound

This paper cites Hyperbolic entailment cones for learning hierarchical em- beddings.

Global and Local Entailment Learning for Natural World Imagery Hyperbolic entailment cones for learning hierarchical em- beddings

Reference 14

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Observation 548c7539-436c-4148-a4bf-625c16547190 · outbound

This paper cites Lowe, Graham W.

Global and Local Entailment Learning for Natural World Imagery Lowe, Graham W

Reference 15

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Observation 17d50def-9bd9-4ca8-ae88-7d1b77cec287 · outbound

This paper cites Pigeon: Predicting image geolocations.

Global and Local Entailment Learning for Natural World Imagery Pigeon: Predicting image geolocations

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:40.062364Z

Source-reported events for the cited work

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Observation 0ad4fd8f-94b2-4d54-8b64-64cdd7640f6f · outbound

This paper cites Contrastive ground-level image and remote sensing pre- training improves representation learning for natural world imagery.

Global and Local Entailment Learning for Natural World Imagery Contrastive ground-level image and remote sensing pre- training improves representation learning for natural world imagery

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:39.879393Z

Source-reported events for the cited work

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Observation d577da68-242d-4ff7-af68-084fbb0ff9cc · outbound

This paper cites Open- clip, 2021.

Global and Local Entailment Learning for Natural World Imagery Open- clip, 2021

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:39.719041Z

Source-reported events for the cited work

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Observation ff99ac94-0e0c-46ed-b85b-7c744ed35a9f · outbound

This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

Global and Local Entailment Learning for Natural World Imagery Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 19

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Observation 01eb54e7-67f3-4298-af7b-b6d533fc3fe8 · outbound

This paper cites Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings.

Global and Local Entailment Learning for Natural World Imagery Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings

Reference 20

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Observation 846f7b7c-4c4b-4f1f-a647-c8ed869a4e9d · outbound

This paper cites 9 Align before fuse: Vision and language representation learn- ing with momentum distillation.

Global and Local Entailment Learning for Natural World Imagery 9 Align before fuse: Vision and language representation learn- ing with momentum distillation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:39.577439Z

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Observation 1366ec4b-864e-40e3-aa18-eec60a35be1b · outbound

This paper cites Fine-grained semantically aligned vision-language pre-training.

Global and Local Entailment Learning for Natural World Imagery Fine-grained semantically aligned vision-language pre-training

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:39.453591Z

Source-reported events for the cited work

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Observation ecd24597-3e4e-4509-aee6-0abbdf9ed4e8 · outbound

This paper cites Maruf, Arka Daw, Kazi Sajeed Mehrab, Harish Babu Manogaran, Abhilash Neog, Medha Sawhney, Mridul Khu- rana, James P.

Global and Local Entailment Learning for Natural World Imagery Maruf, Arka Daw, Kazi Sajeed Mehrab, Harish Babu Manogaran, Abhilash Neog, Medha Sawhney, Mridul Khu- rana, James P

Reference 23

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Observation a3bd064c-3c80-479b-a405-5813f35c1596 · outbound

This paper cites Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion.

Global and Local Entailment Learning for Natural World Imagery Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion

Reference 24

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Observation 026bbf44-9cb0-4e7a-8d5f-18d924bf1475 · outbound

This paper cites Animal kingdom: A large and diverse dataset for animal behavior understanding.

Global and Local Entailment Learning for Natural World Imagery Animal kingdom: A large and diverse dataset for animal behavior understanding

Reference 25

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Observation c8894a8f-6133-49af-9e66-2d0639ebae1d · outbound

This paper cites Poincar ´e embeddings for learning hierarchical representations.

Global and Local Entailment Learning for Natural World Imagery Poincar ´e embeddings for learning hierarchical representations

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:38.847600Z

Source-reported events for the cited work

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Observation 3725d289-5144-4fd9-a6e4-4e95404265bc · outbound

This paper cites Learning continuous hierarchies in the lorentz model of hyperbolic geometry.

Global and Local Entailment Learning for Natural World Imagery Learning continuous hierarchies in the lorentz model of hyperbolic geometry

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:38.703237Z

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Observation e9484950-50ac-4684-9041-5844c40453f5 · outbound

This paper cites Compositional entailment learning for hyperbolic vision-language models.

Global and Local Entailment Learning for Natural World Imagery Compositional entailment learning for hyperbolic vision-language models

Reference 28

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Observation 761e23be-e08d-4434-9bb0-5da276d7bff6 · outbound

This paper cites Har- nessing artificial intelligence to fill global shortfalls in biodi- versity knowledge.

Global and Local Entailment Learning for Natural World Imagery Har- nessing artificial intelligence to fill global shortfalls in biodi- versity knowledge

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:38.345424Z

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Observation 37462d67-1a16-4468-8606-c8703c6aa76f · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Global and Local Entailment Learning for Natural World Imagery Learning transferable visual models from natural language supervi- sion

Reference 30

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Observation 0b2da370-685e-4faa-be53-2e86d5dcf278 · outbound

This paper cites Accept the modality gap: An exploration in the hyperbolic space.

Global and Local Entailment Learning for Natural World Imagery Accept the modality gap: An exploration in the hyperbolic space

Reference 31

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Observation a6e64047-eade-485a-86ec-0d82706ada24 · outbound

This paper cites Accelerating ocean species discovery and laying the foundations for the future of marine biodiver- sity research and monitoring.

Global and Local Entailment Learning for Natural World Imagery Accelerating ocean species discovery and laying the foundations for the future of marine biodiver- sity research and monitoring

Reference 32

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Observation eb488fa6-56a0-4387-b2c4-bd768b999cec · outbound

This paper cites Birdsat: Cross-view contrastive masked autoencoders for bird species classification and mapping.

Global and Local Entailment Learning for Natural World Imagery Birdsat: Cross-view contrastive masked autoencoders for bird species classification and mapping

Reference 33

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Observation ecf4acab-692e-470b-9945-2367d23b4440 · outbound

This paper cites Taxabind: A unified embedding space for ecological applications.

Global and Local Entailment Learning for Natural World Imagery Taxabind: A unified embedding space for ecological applications

Reference 34

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Observation c4b6d7f0-62ad-4f81-a11e-90a3e8c51aab · outbound

This paper cites Deepwild: Wildlife identification, localisation and estima- tion on camera trap videos using deep learning.

Global and Local Entailment Learning for Natural World Imagery Deepwild: Wildlife identification, localisation and estima- tion on camera trap videos using deep learning

Reference 35

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Observation b8d4410c-a531-4846-aa8e-6629d50bfc3d · outbound

This paper cites Bioclip: A vision foundation model for the tree of life.

Global and Local Entailment Learning for Natural World Imagery Bioclip: A vision foundation model for the tree of life

Reference 36

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Observation 8424fb22-1491-4915-9ce3-114fed5de528 · outbound

This paper cites Semi-Supervised Learning with Taxonomic Labels.

Global and Local Entailment Learning for Natural World Imagery Semi-Supervised Learning with Taxonomic Labels

Reference 37

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Observation 8339fde5-67b6-453d-977e-a1b978561d1f · outbound

This paper cites Poincar\'e GloVe: Hyperbolic Word Embeddings.

Global and Local Entailment Learning for Natural World Imagery Poincar\'e GloVe: Hyperbolic Word Embeddings

Reference 38

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Source-reported events for the cited work

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This paper cites The inaturalist species classification and de- tection dataset.

Global and Local Entailment Learning for Natural World Imagery The inaturalist species classification and de- tection dataset

Reference 39

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Source-reported events for the cited work

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Observation 7064894b-fd49-4068-9b58-702c8c023b2d · outbound

This paper cites Order-Embeddings of Images and Language.

Global and Local Entailment Learning for Natural World Imagery Order-Embeddings of Images and Language

Reference 40

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This paper cites Jones, Oisin Mac Aodha, Sara Beery, and Grant Van Horn.

Global and Local Entailment Learning for Natural World Imagery Jones, Oisin Mac Aodha, Sara Beery, and Grant Van Horn

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:37.095687Z

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Observation 257ccfc2-22ac-4b8b-a7ac-a11a87924b4e · outbound

This paper cites Inquire: A natural world text-to-image retrieval benchmark.

Global and Local Entailment Learning for Natural World Imagery Inquire: A natural world text-to-image retrieval benchmark

Reference 42

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Observation bcdaa094-6d18-485f-a166-d31964673ebb · outbound

This paper cites Geoclip: Clip-inspired alignment be- tween locations and images for effective worldwide geo- localization.

Global and Local Entailment Learning for Natural World Imagery Geoclip: Clip-inspired alignment be- tween locations and images for effective worldwide geo- localization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:36.801590Z

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Observation a1829513-dd65-4fe3-9f4c-c670d6c30f1e · outbound

This paper cites 10 Learning visual hierarchies with hyperbolic embeddings.

Global and Local Entailment Learning for Natural World Imagery 10 Learning visual hierarchies with hyperbolic embeddings

Reference 44

Resolution: verified exact; raw_fallback, observed 2026-08-06T22:33:35.898214Z

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Observation d717c493-1392-401a-8b25-5196cfbfdaeb · outbound

This paper cites Biotrove: A large curated image dataset enabling ai for bio- diversity.

Global and Local Entailment Learning for Natural World Imagery Biotrove: A large curated image dataset enabling ai for bio- diversity

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:36.663179Z

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Observation aeff4d42-cf50-4119-b915-40f1fc14ae37 · outbound

This paper cites FILIP: Fine-grained Interactive Language-Image Pre-Training.

Global and Local Entailment Learning for Natural World Imagery FILIP: Fine-grained Interactive Language-Image Pre-Training

Reference 46

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Observation 5d73b2f0-8ef4-4411-a7c4-7967d85d2adb · outbound

This paper cites Shadow Cones: A Generalized Framework for Partial Order Embeddings.

Global and Local Entailment Learning for Natural World Imagery Shadow Cones: A Generalized Framework for Partial Order Embeddings

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:33:35.615951Z

Source-reported events for the cited work

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Observation 313efdb7-6b36-4625-b55a-b6b3255b457b · outbound

This paper cites an unresolved cited work.

Global and Local Entailment Learning for Natural World Imagery Unresolved cited work

Reference 48

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Source-reported events for the cited work

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Pith citing papers

Observation 95454e06-4331-4bbe-8af5-073e2ee9594b · inbound

Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning cites this paper.

Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning Global and Local Entailment Learning for Natural World Imagery

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:26:08.152519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.