Paper Citation Record · LEDGER

Expanding Zero-Shot Object Counting with Rich Prompts

As of 20 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.15398.

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

pith.paper-citation-record.v1
2505.15398 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:21:53.426387Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

41 of 41 outbound references displayed

verified exact0
verified fuzzy29
unresolved11
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 84825dde-251d-4c10-a246-855a04e71db9 · outbound

This paper cites Open-world Text-specified Object Counting.

Expanding Zero-Shot Object Counting with Rich Prompts Open-world Text-specified Object Counting

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:49.431258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46cd3e59-3f3c-4a2d-ae40-60d9c39d5266 · outbound

This paper cites CountGD: Multi-Modal Open-World Counting.

Expanding Zero-Shot Object Counting with Rich Prompts CountGD: Multi-Modal Open-World Counting

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:49.480351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9dea2f3-bb6d-4312-a5c9-3de9136dda9d · outbound

This paper cites The claude 3 model family: Opus, sonnet, haiku,.

Expanding Zero-Shot Object Counting with Rich Prompts The claude 3 model family: Opus, sonnet, haiku,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:56.098987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2adba92c-4fd2-4aa6-812a-2371dfc57bc8 · outbound

This paper cites Lempitsky, and Andrew Zisserman.

Expanding Zero-Shot Object Counting with Rich Prompts Lempitsky, and Andrew Zisserman

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:56.070357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c5507ccf-988c-434e-862e-45492514eafd · outbound

This paper cites Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic.

Expanding Zero-Shot Object Counting with Rich Prompts Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:49.783846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 355eea45-b820-4fda-9a77-cfbc19796db2 · outbound

This paper cites A low-shot object counting network with iterative prototype adaptation.

Expanding Zero-Shot Object Counting with Rich Prompts A low-shot object counting network with iterative prototype adaptation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:56.047762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 87c29022-3431-422d-9ede-ec3805910242 · outbound

This paper cites Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision.

Expanding Zero-Shot Object Counting with Rich Prompts Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:50.159649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 144b58c8-fe23-443f-a176-e69cf0dc3691 · outbound

This paper cites an unresolved cited work.

Expanding Zero-Shot Object Counting with Rich Prompts Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:21:56.020925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cba5ff49-fdd8-4454-b4d0-911efb2155ac · outbound

This paper cites Point, segment and count: A general- ized framework for object counting.

Expanding Zero-Shot Object Counting with Rich Prompts Point, segment and count: A general- ized framework for object counting

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.999745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6d9d7fd4-db31-4a5d-bf5c-280cda7166c4 · outbound

This paper cites T-Rex: Counting by Visual Prompting.

Expanding Zero-Shot Object Counting with Rich Prompts T-Rex: Counting by Visual Prompting

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:50.614394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd9f2401-7957-4299-97d7-97c0ce118f0c · outbound

This paper cites Clip-count: Towards text-guided zero-shot object counting.

Expanding Zero-Shot Object Counting with Rich Prompts Clip-count: Towards text-guided zero-shot object counting

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.974409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 00fd02e0-828c-4f9d-8150-e19f6200c8f3 · outbound

This paper cites Vlcounter: Text-aware visual representation for zero- shot object counting.

Expanding Zero-Shot Object Counting with Rich Prompts Vlcounter: Text-aware visual representation for zero- shot object counting

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.949379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ef1e58f7-64c4-4e3f-9be3-769a0c60fbad · outbound

This paper cites LISA: reasoning segmentation via large language model.

Expanding Zero-Shot Object Counting with Rich Prompts LISA: reasoning segmentation via large language model

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.928088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7cda651f-1c05-4bb7-80e0-a3c5d4d1d89d · outbound

This paper cites Crowdclip: Unsupervised crowd counting via vision-language model.

Expanding Zero-Shot Object Counting with Rich Prompts Crowdclip: Unsupervised crowd counting via vision-language model

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.902742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 71dd4059-a3a9-469b-afdb-d75dcacde6af · outbound

This paper cites Countr: Transformer-based generalised visual counting.

Expanding Zero-Shot Object Counting with Rich Prompts Countr: Transformer-based generalised visual counting

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.877260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 46d927af-b833-4ee9-a1c5-0f1919e88351 · outbound

This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Expanding Zero-Shot Object Counting with Rich Prompts Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:51.549559Z

Source-reported events for the cited work

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Observation c343aff6-a3c3-4568-b147-6e0238964bd5 · outbound

This paper cites Class-agnostic counting.

Expanding Zero-Shot Object Counting with Rich Prompts Class-agnostic counting

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.852893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6502b0bd-aff7-4eb7-ab35-f5df536ead3a · outbound

This paper cites Nathan Mundhenk, Goran Konjevod, Wesam A.

Expanding Zero-Shot Object Counting with Rich Prompts Nathan Mundhenk, Goran Konjevod, Wesam A

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.832587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 60c8f5bf-d4e0-4c61-bd31-3bd203c0470c · outbound

This paper cites GPT-4 Technical Report.

Expanding Zero-Shot Object Counting with Rich Prompts GPT-4 Technical Report

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:51.932068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1fdb9c7-29e6-49e5-ac24-3719cf2f67f8 · outbound

This paper cites Teaching CLIP to count to ten.

Expanding Zero-Shot Object Counting with Rich Prompts Teaching CLIP to count to ten

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.810691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93ed0d45-e899-478c-a4d2-0e5ee70ca8a5 · outbound

This paper cites Kosmos-2: Grounding Multimodal Large Language Models to the World.

Expanding Zero-Shot Object Counting with Rich Prompts Kosmos-2: Grounding Multimodal Large Language Models to the World

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:52.113515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c3f6eef-f4d8-4ba5-858f-341dcb758acf · outbound

This paper cites Learning to count everything.

Expanding Zero-Shot Object Counting with Rich Prompts Learning to count everything

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.787062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93ddbfe0-89fc-4942-9340-b02792d0b143 · outbound

This paper cites Shaker, Salman H.

Expanding Zero-Shot Object Counting with Rich Prompts Shaker, Salman H

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.766602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b997638b-fc13-46cb-b74a-8e0360fddfbb · outbound

This paper cites Pixellm: Pixel reasoning with large multimodal model.

Expanding Zero-Shot Object Counting with Rich Prompts Pixellm: Pixel reasoning with large multimodal model

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.745754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bce5b133-9df8-430e-bc54-b6d2cb3833d9 · outbound

This paper cites Sindagi, R.

Expanding Zero-Shot Object Counting with Rich Prompts Sindagi, R

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.709396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 04ca6f2d-ef08-4923-a270-8b255a27ae4f · outbound

This paper cites Represent, compare, and learn: A similarity-aware framework for class-agnostic counting.

Expanding Zero-Shot Object Counting with Rich Prompts Represent, compare, and learn: A similarity-aware framework for class-agnostic counting

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.647466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0988215a-bc8b-4269-88a6-47aa7e82b0d3 · outbound

This paper cites Degpr: Deep guided posterior regularization for multi-class cell detection and counting.

Expanding Zero-Shot Object Counting with Rich Prompts Degpr: Deep guided posterior regularization for multi-class cell detection and counting

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.511930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c1809163-1013-4c62-9cca-02cf59787cb3 · outbound

This paper cites Visionllm: Large language model is also an open-ended decoder for vision-centric tasks.

Expanding Zero-Shot Object Counting with Rich Prompts Visionllm: Large language model is also an open-ended decoder for vision-centric tasks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.379595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 57118b4f-d2c6-462e-952f-528493d5f1fe · outbound

This paper cites Vi- sion transformer off-the-shelf: A surprising baseline for few- shot class-agnostic counting.

Expanding Zero-Shot Object Counting with Rich Prompts Vi- sion transformer off-the-shelf: A surprising baseline for few- shot class-agnostic counting

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.199013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 45690844-0bd1-4be0-9f80-775804bcfbcd · outbound

This paper cites Zero-shot object counting.

Expanding Zero-Shot Object Counting with Rich Prompts Zero-shot object counting

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.125372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d6665ee3-f423-49b6-88c7-d9901aff327f · outbound

This paper cites Zero-Shot Object Counting with Language-Vision Models.

Expanding Zero-Shot Object Counting with Rich Prompts Zero-Shot Object Counting with Language-Vision Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:52.770034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b916102c-69b2-4ed0-8280-8e66a7135996 · outbound

This paper cites Hsu, and Wen- Chin Chen.

Expanding Zero-Shot Object Counting with Rich Prompts Hsu, and Wen- Chin Chen

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:55.037065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 943a33e0-3d2a-40f4-9e73-63c1b03558da · outbound

This paper cites GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest.

Expanding Zero-Shot Object Counting with Rich Prompts GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T15:21:52.852001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6171d22d-2321-4585-b1f7-9bd8f0b2b2a7 · outbound

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

Expanding Zero-Shot Object Counting with Rich Prompts Single-image crowd counting via multi-column convolutional neural network

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:54.877032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation de66e638-ad98-4cd0-8930-4fe057f649ba · outbound

This paper cites Zero-shot object counting with good exemplars.

Expanding Zero-Shot Object Counting with Rich Prompts Zero-shot object counting with good exemplars

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:54.727307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b2a77c66-5076-4721-8c21-812f8c74a905 · outbound

This paper cites 2) • Extended visualizations of density maps (Sec.

Expanding Zero-Shot Object Counting with Rich Prompts 2) • Extended visualizations of density maps (Sec

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:54.596312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b95764ac-91e4-491b-b4a2-6f8f257aa5b0 · outbound

This paper cites an unresolved cited work.

Expanding Zero-Shot Object Counting with Rich Prompts Unresolved cited work

Reference 37

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T15:21:54.561251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c261f19a-e54b-4073-bbf9-687da48ef316 · outbound

This paper cites 9 illustrates RichCount’s performance on CARPK [8] and SHANGHAI TECH [34].

Expanding Zero-Shot Object Counting with Rich Prompts 9 illustrates RichCount’s performance on CARPK [8] and SHANGHAI TECH [34]

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:54.421468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d6d9844d-5acc-451a-8ca6-8fd71b572217 · outbound

This paper cites 7 presents an ablation study on FSC-147 , comparing the use of basic category labels ( Class), detailed descrip- tions (Des), and generic terms ( Des-f).

Expanding Zero-Shot Object Counting with Rich Prompts 7 presents an ablation study on FSC-147 , comparing the use of basic category labels ( Class), detailed descrip- tions (Des), and generic terms ( Des-f)

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:54.242306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 121a1be2-4a22-4384-900c-2dc5faaf65a0 · outbound

This paper cites 8 illustrates the impact of various margin values on image-text alignment performance during training.

Expanding Zero-Shot Object Counting with Rich Prompts 8 illustrates the impact of various margin values on image-text alignment performance during training

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:54.050431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7b0cea89-8d11-41ca-98de-00dbc7edf359 · outbound

This paper cites 9 illustrates the impact of various FFN structures on the expressiveness of image and text features.

Expanding Zero-Shot Object Counting with Rich Prompts 9 illustrates the impact of various FFN structures on the expressiveness of image and text features

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:21:53.971215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.