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Paper Citation Record · LEDGER

One-Shot Crowd Counting With Density Guidance For Scene Adaptation

As of 23 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2602.07955.

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

pith.paper-citation-record.v1
2602.07955 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:29:34.999362Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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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

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  • unresolved41
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External citation measurements

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Outbound references

Observation b79f74e4-63c0-4dc8-8162-692149663fcb · outbound

This paper cites Divide and grow: Capturing huge diversity in crowd images with incrementally growing cnn.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Divide and grow: Capturing huge diversity in crowd images with incrementally growing cnn

Reference 1

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Observation fa680799-9bdf-4468-af61-65e6700258da · outbound

This paper cites Semantic generative augmentations for few-shot counting.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Semantic generative augmentations for few-shot counting

Reference 8

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Observation e0cc679d-28da-48c2-ae2f-451049fe0375 · outbound

This paper cites Domain-general crowd counting in unseen scenarios.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Domain-general crowd counting in unseen scenarios

Reference 9

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Observation 92b76ca1-335f-4044-ab26-8cca7a0612a1 · outbound

This paper cites CNN-based Density Estimation and Crowd Counting: A Survey.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation CNN-based Density Estimation and Crowd Counting: A Survey

Reference 10

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Observation d075c312-1b9f-440e-b06c-6f300ea45ada · outbound

This paper cites Kang and A.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Kang and A

Reference 12

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Observation df83e6c2-256b-42ef-8459-9fe752f31c40 · outbound

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

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Vlcounter: Text-aware visual repre- sentation for zero-shot object counting

Reference 13

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Observation 80b4aa62-fca0-4eff-89b4-9b3cebd36a86 · outbound

This paper cites Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection

Reference 14

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Observation 26fec5af-cef4-4265-958b-4b92a38e8245 · outbound

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

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Crowdclip: Unsupervised crowd counting via vision-language model

Reference 16

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Observation 5ca1eb2e-69eb-4b72-82cf-0ae9c80aece6 · outbound

This paper cites Attentive crowd flow machines.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Attentive crowd flow machines

Reference 17

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Observation 23291cca-8a72-4d18-a92c-8cd678d3c79a · outbound

This paper cites an unresolved cited work.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Unresolved cited work

Reference 18

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Observation 095a570f-57bd-4900-9965-cad98048ae37 · outbound

This paper cites Teaching clip to count to ten.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Teaching clip to count to ten

Reference 21

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Observation 5937e8cc-e19f-4772-a5c4-21ea40eb9650 · outbound

This paper cites Switchable whitening for deep representation learning.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Switchable whitening for deep representation learning

Reference 22

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Observation c4e20529-72ec-41f6-9f6f-73e85f1973cc · outbound

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

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Dave - a detect-and-verify paradigm for low-shot counting

Reference 23

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Observation 2444bf5d-b848-4a56-bdef-22a204671b58 · outbound

This paper cites Gary Chan.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Gary Chan

Reference 24

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Observation 27a62437-30c9-4fda-8607-2ad4c1f3948d · outbound

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One-Shot Crowd Counting With Density Guidance For Scene Adaptation Unresolved cited work

Reference 25

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Observation e73edc1e-8842-4be4-9fe1-eaca4c98d290 · outbound

This paper cites Learning to count everything.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Learning to count everything

Reference 26

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Observation 1c263896-d055-4651-8d85-da5b90ec5fa6 · outbound

This paper cites Optimization as a model for few-shot learning.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Optimization as a model for few-shot learning

Reference 27

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Observation 89137b69-c5fd-4105-81f0-6253332b41d9 · outbound

This paper cites Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection

Reference 29

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Observation bc208c6c-0352-4f0e-9842-7348e5e0448e · outbound

This paper cites A compre- hensive survey of few-shot learning: Evolution, applica- tions, challenges, and opportunities.ACM Computing Sur- veys, 55(13s):1–40,.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation A compre- hensive survey of few-shot learning: Evolution, applica- tions, challenges, and opportunities.ACM Computing Sur- veys, 55(13s):1–40,

Reference 30

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Observation f7f26fdb-003d-49b2-9e1b-3d1e9e4abc70 · outbound

This paper cites Language-guided zero-shot object counting.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Language-guided zero-shot object counting

Reference 31

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Observation 060144e6-6038-44bf-830b-3be392495ecf · outbound

This paper cites Detection, tracking, and counting meets drones in crowds: A benchmark.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Detection, tracking, and counting meets drones in crowds: A benchmark

Reference 32

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Observation 71d67f1c-198c-4d71-878b-2c08395f8ca6 · outbound

This paper cites Learning spatial similarity distribution for few- shot object counting.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Learning spatial similarity distribution for few- shot object counting

Reference 33

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Observation e5583150-1d1e-433d-965e-1013d5096a9a · outbound

This paper cites Prototype mixture models for few-shot semantic segmentation.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Prototype mixture models for few-shot semantic segmentation

Reference 34

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Observation 661f7a83-8378-4fbc-a6f3-cd915af5f69a · outbound

This paper cites Detclipv3: Towards versatile generative open- vocabulary object detection.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Detclipv3: Towards versatile generative open- vocabulary object detection

Reference 35

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Observation 99676cc9-8ede-4c7e-904c-de1910ad52ab · outbound

This paper cites Few-shot object counting with similarity-aware feature enhancement.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Few-shot object counting with similarity-aware feature enhancement

Reference 36

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Observation 79710791-09e9-445d-a912-ca42acf450ce · outbound

This paper cites Ro- bust head-shoulder detection by pca-based multilevel hog- lbp detector for people counting.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Ro- bust head-shoulder detection by pca-based multilevel hog- lbp detector for people counting

Reference 37

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Observation fcbcfe44-4d7a-46cf-b87a-5d786ccd9bb7 · outbound

This paper cites Daot: Domain-agnostically aligned optimal transport for domain-adaptive crowd counting.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Daot: Domain-agnostically aligned optimal transport for domain-adaptive crowd counting

Reference 41

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Observation 3b0622f4-eb59-4aec-a1b8-b475d4fdee2f · outbound

This paper cites Learning to generalize: Meta- learning for domain generalization.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Learning to generalize: Meta- learning for domain generalization

Reference 2008

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Observation ea105312-e605-4d77-b01c-155649808d47 · outbound

This paper cites Cross-scene crowd counting via deep convolutional neural networks.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Cross-scene crowd counting via deep convolutional neural networks

Reference 2010

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Observation e928a5c4-b081-4dc6-ab33-f87dc304cc50 · outbound

This paper cites Multi-task semi-supervised crowd counting via global to local self-correction.Pattern Recognition, 140:109506,.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Multi-task semi-supervised crowd counting via global to local self-correction.Pattern Recognition, 140:109506,

Reference 2014

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Observation cb24163b-1ad8-4e84-90c8-bdecb6333cfc · outbound

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

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Single-image crowd counting via multi-column convolutional neural network

Reference 2015

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Observation fcfd715a-cad5-4b5a-9ad9-8c7a030fe701 · outbound

This paper cites Canet: Class-agnostic seg- mentation networks with iterative refinement and atten- tive few-shot learning.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Canet: Class-agnostic seg- mentation networks with iterative refinement and atten- tive few-shot learning

Reference 2016

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Observation f3ea840a-57ba-4fc8-aba4-4096225aee9e · outbound

This paper cites Few- shot scene adaptive crowd counting using meta-learning.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Few- shot scene adaptive crowd counting using meta-learning

Reference 2017

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Observation 025068b9-17ba-4b45-bcfe-3726751935a2 · outbound

This paper cites Ten years of pedes- trian detection, what have we learned? InEuropean Conference on Computer Vision, pages 613–627.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Ten years of pedes- trian detection, what have we learned? InEuropean Conference on Computer Vision, pages 613–627

Reference 2018

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source=pdf_text observed=2026-08-03T03:29:32.701045Z digest=sha256:0e36668c1ab3be69b6611d1d05cd3ff662ec13b926b9db2b3fb2b4447747c7d7

Observation cef6dd19-81cf-4d54-aa8c-e62fceb9ac74 · outbound

This paper cites Dynamic prototype convolution network for few-shot semantic seg- mentation.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Dynamic prototype convolution network for few-shot semantic seg- mentation

Reference 2019

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Observation c3ed6238-aa1b-422f-b98a-9153f19e05d8 · outbound

This paper cites A survey of deep learning methods for density estimation and crowd counting.Vici- nagearth, 2(1):1–37,.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation A survey of deep learning methods for density estimation and crowd counting.Vici- nagearth, 2(1):1–37,

Reference 2020

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source=pdf_text observed=2026-08-03T03:29:33.302788Z digest=sha256:ae6e856f9192cf30c02292aaba74cce40eb2e609cdbc1546d4744526171d6553

Observation 75920329-2087-49d7-9fc3-be4567aafa8c · outbound

This paper cites Few-shot semantic segmentation with prototype learning.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Few-shot semantic segmentation with prototype learning

Reference 2021

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source=pdf_text observed=2026-08-03T03:29:33.033891Z digest=sha256:0f6b8db9d8cf4411d7f8d6030be629a5eaa611f17b90003038aa93da2045d26d

Observation 3db29d3b-9924-42c7-9f49-781d2d0df24c · outbound

This paper cites Background noise filtering and distribution dividing for crowd counting.IEEE Transactions on Image Processing, 29:8199–8212,.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Background noise filtering and distribution dividing for crowd counting.IEEE Transactions on Image Processing, 29:8199–8212,

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-03T03:29:34.174944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:29:34.174944Z digest=sha256:3e4b5f11138297bf2eb91a7e097b3d899e26e04a92881056afe9451ef633a9cf

Observation 34d49cfb-cba7-416e-b9c9-67e0a045fb7f · outbound

This paper cites One- shot any-scene crowd counting with local-to-global guid- ance.IEEE Transactions on Image Processing, 33:6622– 6632,.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation One- shot any-scene crowd counting with local-to-global guid- ance.IEEE Transactions on Image Processing, 33:6622– 6632,

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-03T03:29:32.800070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:29:32.800070Z digest=sha256:7dff81064e6db14e01dd4cd20682291428b7225e6496a6adf40efe8fe9e35550

Observation 5399479d-6494-4aff-b05f-0f70008ead57 · outbound

This paper cites Crowd counting with crowd attention convolutional neural network.Neurocomputing, 382:210–220,.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Crowd counting with crowd attention convolutional neural network.Neurocomputing, 382:210–220,

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-03T03:29:32.898493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:29:32.898493Z digest=sha256:68d8d3161b2f81217589a032fe05cee42c7e931629755172865d16abc2022eb5

Observation 5233150e-96fb-4ce7-8424-75aaaba1fbd2 · outbound

This paper cites Robustnet: Improving domain generalization in urban- scene segmentation via instance selective whitening.

One-Shot Crowd Counting With Density Guidance For Scene Adaptation Robustnet: Improving domain generalization in urban- scene segmentation via instance selective whitening

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-03T03:29:32.974610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:29:32.974610Z digest=sha256:d5481539b950a4efcbe40d7c3704c391c13b2755924074edb2d07c2783c43b6f

Pith citing papers

No inbound Pith citation observations are available.