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Paper Citation Record · LEDGER

Spatially-Aware Class-Agnostic Object Counting

As of 23 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2607.16826.

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

pith.paper-citation-record.v1
2607.16826 v2

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T19:51:53.627427Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

21 of 21 outbound references displayed

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  • unresolved21
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2ab2ec36-193a-4377-ad5a-ce7774d0e343 · outbound

This paper cites Single-image crowd counting via multi-column convolutional neural network,.

Spatially-Aware Class-Agnostic Object Counting Single-image crowd counting via multi-column convolutional neural network,

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T19:51:52.281861Z digest=sha256:2acebe3243b805819857777505111cf381af2724693dabae036758613858a2ce

Observation ebe5cb02-5b41-4ef3-b824-b04db573bb06 · outbound

This paper cites A large contextual dataset for classification, detection and counting of cars with deep learning,.

Spatially-Aware Class-Agnostic Object Counting A large contextual dataset for classification, detection and counting of cars with deep learning,

Reference 2

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source=pdf_text observed=2026-08-01T19:51:52.390319Z digest=sha256:199b69122cbeded0b1fbd8019efadad3f0c29b741441156b72c8951fc53f4406

Observation 3fd55d05-0bf8-4f74-8796-31b6b4fdc749 · outbound

This paper cites CounTR: Transformer-based Generalised Visual Counting.

Spatially-Aware Class-Agnostic Object Counting CounTR: Transformer-based Generalised Visual Counting

Reference 3

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source=pdf_text observed=2026-08-01T19:51:52.487383Z digest=sha256:bd3a1224058ac5e8e88b80e2338d4765768ea451425cbd73ba76d55e516dd357

Observation d983de8b-98d0-405c-b671-1e8670548255 · outbound

This paper cites Class-agnostic counting,.

Spatially-Aware Class-Agnostic Object Counting Class-agnostic counting,

Reference 4

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source=pdf_text observed=2026-08-01T19:51:52.594894Z digest=sha256:7982be0838aaf158dcc81bf1321ac3691ec12e180309b1bcc8295e1def8cc1a1

Observation 9bf4d679-3e47-46c1-af8c-61e5be002197 · outbound

This paper cites Vision transformers for dense prediction,.

Spatially-Aware Class-Agnostic Object Counting Vision transformers for dense prediction,

Reference 5

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T19:51:52.748585Z digest=sha256:5bff0dcd27003cd106d542765ebb08b24cc3acc8cc1587881b435469f6d78bbd

Observation 3f153934-fd9d-459c-ae0c-79689c3d5136 · outbound

This paper cites Featup: A model-agnostic framework for features at any resolution,.

Spatially-Aware Class-Agnostic Object Counting Featup: A model-agnostic framework for features at any resolution,

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T19:51:52.789789Z digest=sha256:d52b197291506a38db237f70d31242c2894c58d8382df31ec68c37964ab75243

Observation 386b4942-171c-443e-8b9f-845893fcaddd · outbound

This paper cites Learning to count everything,.

Spatially-Aware Class-Agnostic Object Counting Learning to count everything,

Reference 7

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source=pdf_text observed=2026-08-01T19:51:52.840255Z digest=sha256:76f94d213681ceebd00ad3984bf61e5439c7db0784e87871ba5b01d89fdd4c39

Observation 48511f04-1d75-44c5-ab34-96defcbe5d05 · outbound

This paper cites Zero-shot object counting,.

Spatially-Aware Class-Agnostic Object Counting Zero-shot object counting,

Reference 8

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source=pdf_text observed=2026-08-01T19:51:52.884430Z digest=sha256:61b21f9a4a2c303c5218ef97ff92bdba375d631b011a27753620fd65ed44f214

Observation 06662335-568c-4308-9fa8-c3520b07c564 · outbound

This paper cites Exemplar free class agnostic counting,.

Spatially-Aware Class-Agnostic Object Counting Exemplar free class agnostic counting,

Reference 9

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source=pdf_text observed=2026-08-01T19:51:52.931796Z digest=sha256:3aecb3e731eb3a742adc98069b6215e7eb9cda66c7c0182017de8acc9693af00

Observation 677013aa-ab5e-4f31-97f0-663c4e57f39d · outbound

This paper cites CLIP-Count: Towards Text-Guided Zero-Shot Object Counting.

Spatially-Aware Class-Agnostic Object Counting CLIP-Count: Towards Text-Guided Zero-Shot Object Counting

Reference 10

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source=pdf_text observed=2026-08-01T19:51:52.994022Z digest=sha256:fee95420850b263951728d623c5aae066168c9cdddc62c53e4f078e566268f25

Observation 383ace9c-4127-41bd-87ed-d4cc7d0a0861 · outbound

This paper cites Open- world text-specified object counting,.

Spatially-Aware Class-Agnostic Object Counting Open- world text-specified object counting,

Reference 11

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source=pdf_text observed=2026-08-01T19:51:53.067879Z digest=sha256:0dcefdaac3d389eac4579ac3e348b378ef06bf50357009d318f684d4e52febee

Observation d51f7f20-eb53-4787-9866-06479b20a048 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Spatially-Aware Class-Agnostic Object Counting Learning transferable visual models from natural language supervision,

Reference 12

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source=pdf_text observed=2026-08-01T19:51:53.134786Z digest=sha256:82368b263dcb1ac51ebb44cff90b3e7b803600b9817d9c26e87f8f8d50eeaaa4

Observation c5e006e7-dfe5-46c2-abfa-3813be2300ea · outbound

This paper cites Drone-based object counting by spatially regularized regional proposal network,.

Spatially-Aware Class-Agnostic Object Counting Drone-based object counting by spatially regularized regional proposal network,

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T19:51:53.193311Z digest=sha256:9c6e046445907b0659f95a20b94f6aa18709b7af97485d7dd84a0be0251f08cc

Observation 13c82bd4-519a-4a58-a76f-0e2b6217251d · outbound

This paper cites an unresolved cited work.

Spatially-Aware Class-Agnostic Object Counting Unresolved cited work

Reference 14

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source=pdf_text observed=2026-08-01T19:51:52.672923Z digest=sha256:3e3ffb77d46cb67bdd062b6a3c38c1e62e18488d8862c60ea60bb7ffe2cf4efb

Observation 1cbab7d0-e13c-4a67-90ca-dbecac34559b · outbound

This paper cites T2icount: Enhancing cross-modal understanding for zero-shot counting,.

Spatially-Aware Class-Agnostic Object Counting T2icount: Enhancing cross-modal understanding for zero-shot counting,

Reference 15

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source=pdf_text observed=2026-08-01T19:51:53.245628Z digest=sha256:54ebfe11c3ddc810e6582ebd47f7027c7ac1988058ca48a878cd4b55f7849967

Observation c7ebb7b3-82bc-403d-881f-9a1e057dc3e2 · outbound

This paper cites Countse: Soft exemplar open- set object counting,.

Spatially-Aware Class-Agnostic Object Counting Countse: Soft exemplar open- set object counting,

Reference 16

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source=pdf_text observed=2026-08-01T19:51:53.286885Z digest=sha256:674b5cc812d844707740c33ef691e0fbd2451a9dbc56674d0192cb0096900247

Observation a10c6f6f-1167-47df-a6b5-f07646158b1a · outbound

This paper cites Boosting quantitive and spatial awareness for zero-shot object counting,.

Spatially-Aware Class-Agnostic Object Counting Boosting quantitive and spatial awareness for zero-shot object counting,

Reference 17

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T19:51:53.354756Z digest=sha256:49be22af48f6363fe43f89aabb1b084f356a00f3013afd5503753bcc612e6421

Observation e0351c11-afdc-450b-bdaa-24014a68690f · outbound

This paper cites A Low-Shot Object Counting Network With Iterative Prototype Adaptation.

Spatially-Aware Class-Agnostic Object Counting A Low-Shot Object Counting Network With Iterative Prototype Adaptation

Reference 18

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source=pdf_text observed=2026-08-01T19:51:53.432849Z digest=sha256:f5e8d86d3fe1d4d9e879568aef7a1f15ba5378b62229934183dd6ca86c60d1ae

Observation 973c4713-a673-4fdb-a1bc-51d2def7dace · outbound

This paper cites Dave-a detect-and-verify paradigm for low-shot counting,.

Spatially-Aware Class-Agnostic Object Counting Dave-a detect-and-verify paradigm for low-shot counting,

Reference 19

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source=pdf_text observed=2026-08-01T19:51:53.489064Z digest=sha256:662c6908f1e378b1743af69c3adc9e8c4cf6db6815f49856e9adf72935660fbe

Observation 1cc8c292-81e7-4a5d-b68b-21d71c131369 · outbound

This paper cites Countgd: Multi-modal open-world counting,.

Spatially-Aware Class-Agnostic Object Counting Countgd: Multi-modal open-world counting,

Reference 20

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source=pdf_text observed=2026-08-01T19:51:53.550601Z digest=sha256:fc03d9ef25a7042ed90f5169bb55df2071dc576b62c45d58810be77e12eae0c9

Observation 81420afe-7e0a-46ca-88b3-edaeccfc3195 · outbound

This paper cites Countingdino: A training-free pipeline for class-agnostic counting using unsupervised backbones,.

Spatially-Aware Class-Agnostic Object Counting Countingdino: A training-free pipeline for class-agnostic counting using unsupervised backbones,

Reference 21

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source=pdf_text observed=2026-08-01T19:51:53.627427Z digest=sha256:328114c59349ff77ed20de04c6e97e96538d84b48eb376c2e65acaad1d50f708

Pith citing papers

No inbound Pith citation observations are available.