Paper Citation Record · LEDGER

Detect Everything with Few Examples

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2309.12969.

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

pith.paper-citation-record.v1
2309.12969 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 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 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:58:54.313179Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:19:48.021479Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e6ad7f42-c487-4313-b2c1-295f968ec37e · inbound

Visual Textualization for Image Prompted Object Detection cites this paper.

Visual Textualization for Image Prompted Object Detection Detect Everything with Few Examples

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T21:37:08.017364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21efc6b6-0b29-4e50-bb40-c0da31f82a08 · inbound

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning cites this paper.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Detect Everything with Few Examples

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T18:42:11.735023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dc68e03-42c5-461d-81af-6a5bd2971bf5 · inbound

Few-Shot Object Detection via Spatial-Channel State Space Model cites this paper.

Few-Shot Object Detection via Spatial-Channel State Space Model Detect Everything with Few Examples

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T15:39:16.279782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e400d1d0-fb08-492c-a14e-4785ff73600e · inbound

MobQA: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answering cites this paper.

MobQA: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answering Detect Everything with Few Examples

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T20:08:53.051705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c55deadb-2c8e-4f61-8182-f252e2209a63 · inbound

VFM-Guided Semi-Supervised Detection Transformer under Source-Free Constraints for Remote Sensing Object Detection cites this paper.

VFM-Guided Semi-Supervised Detection Transformer under Source-Free Constraints for Remote Sensing Object Detection Detect Everything with Few Examples

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T20:10:02.813208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5353a17e-4e4d-43b7-b49a-d768ab2fd76d · inbound

The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results cites this paper.

The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results Detect Everything with Few Examples

Reference 125

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:21:00.699803Z

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.

Observation 11b6818f-6b9e-4d19-b60f-8725efc8651d · inbound

Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection cites this paper.

Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection Detect Everything with Few Examples

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T10:19:48.022781Z

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.

Observation 39f49ab2-44e5-4c23-a6d2-93939e57c348 · inbound

Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection cites this paper.

Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection Detect Everything with Few Examples

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T10:44:36.587221Z

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.

Observation f6d4b407-6fe1-43dc-b760-91c4f7485236 · inbound

Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detection cites this paper.

Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detection Detect Everything with Few Examples

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T00:20:38.061094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9045d735-5f65-4ed0-9390-e8b3cff6a88d · inbound

Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detection cites this paper.

Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detection Detect Everything with Few Examples

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T00:58:54.313179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62ad2285-9d60-44ee-9de5-15b6592e7e95 · inbound

Free-Lunch Augmentation by Revisiting Diffusion-Based Data Generation for Cross-Domain Few-Shot Object Detection cites this paper.

Free-Lunch Augmentation by Revisiting Diffusion-Based Data Generation for Cross-Domain Few-Shot Object Detection Detect Everything with Few Examples

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T00:54:50.984674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.