Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting | Paper Citation Record

Observation c0243e48-16ac-4139-ab58-5372b8459cea · outbound

This paper cites Localization in the crowd with topological con- straints.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Localization in the crowd with topological con- straints

Reference 1

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Observation 91c79937-8701-4c11-b978-d60e97706599 · outbound

This paper cites Anomalous event detection and localization in dense crowd scenes.Mul- timedia Tools and Applications, 82(10):15673–15694, 2023.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Anomalous event detection and localization in dense crowd scenes.Mul- timedia Tools and Applications, 82(10):15673–15694, 2023

Reference 2

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Observation 5c8eec66-cd9a-452f-bf1a-8dae2ec6d5ab · outbound

This paper cites Switching convolutional neural network for crowd counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Switching convolutional neural network for crowd counting

Reference 3

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Observation efedd5a3-7207-4269-8cd4-8945f55599d8 · outbound

This paper cites Bayesian poisson regression for crowd counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Bayesian poisson regression for crowd counting

Reference 4

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Observation c521c2e8-11ff-4a72-ad71-da6c07002f86 · outbound

This paper cites Counting people with low-level features and bayesian regression.IEEE Trans- actions on image processing, 21(4):2160–2177, 2011.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Counting people with low-level features and bayesian regression.IEEE Trans- actions on image processing, 21(4):2160–2177, 2011

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:58.283042Z

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Observation bf4151d6-3c18-407c-b6c9-702c1895fda4 · outbound

This paper cites Privacy preserving crowd monitoring: Counting peo- ple without people models or tracking.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Privacy preserving crowd monitoring: Counting peo- ple without people models or tracking

Reference 6

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Observation c685a124-8a48-4734-9894-c9ea50ef70be · outbound

This paper cites Anchor-based group detection in crowd scenes.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Anchor-based group detection in crowd scenes

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:20.224379Z

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Observation 71e9c19e-fcf6-436d-b164-cbd9475afc86 · outbound

This paper cites Rethinking spatial invariance of convolutional networks for object counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Rethinking spatial invariance of convolutional networks for object counting

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:20.107502Z

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Observation 0b5a907f-c8d2-40ae-ae51-89f51ba711ae · outbound

This paper cites Learning Independent Instance Maps for Crowd Localization.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Learning Independent Instance Maps for Crowd Localization

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:24:06.745017Z

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Observation bfa8fd09-ca59-49bf-855f-a3b5f204711d · outbound

This paper cites Steerer: Resolving scale variations for counting and localization via selective inheritance learning.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Steerer: Resolving scale variations for counting and localization via selective inheritance learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:19.960982Z

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Observation 620cc26c-fed2-4865-854d-48c085db6f2b · outbound

This paper cites Error-aware density isomorphism re- construction for unsupervised cross-domain crowd counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Error-aware density isomorphism re- construction for unsupervised cross-domain crowd counting

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:19.837160Z

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Observation c990206a-99ac-423d-b1ea-9d49b80cc47e · outbound

This paper cites Composition loss for counting, density map estima- tion and localization in dense crowds.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Composition loss for counting, density map estima- tion and localization in dense crowds

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:19.686437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2dd8c908-f1b3-452a-967c-50fe7a39e88b · outbound

This paper cites Ex- plaining convolutional neural networks using softmax gra- dient layer-wise relevance propagation.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Ex- plaining convolutional neural networks using softmax gra- dient layer-wise relevance propagation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:19.530512Z

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Observation 1abff886-ead7-49b1-a141-41b4af021592 · outbound

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

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Clip- count: Towards text-guided zero-shot object counting

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:19.366975Z

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Observation 2efbeb34-629c-42bd-82c0-0c42918b7f11 · outbound

This paper cites Pseudo-label: The simple and effi- cient semi-supervised learning method for deep neural net- works.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Pseudo-label: The simple and effi- cient semi-supervised learning method for deep neural net- works

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:19.230892Z

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Observation c8cf8d2a-d574-48cc-9b7e-448623bfb1f8 · outbound

This paper cites Pedes- trian detection in crowded scenes.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Pedes- trian detection in crowded scenes

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:19.117756Z

Source-reported events for the cited work

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Observation ed8570d5-fcb8-46c4-aa14-adcbc6441028 · outbound

This paper cites Learning to count objects in images.Advances in neural information process- ing systems, 23, 2010.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Learning to count objects in images.Advances in neural information process- ing systems, 23, 2010

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:19.013201Z

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Observation cb902dc8-d7c1-4f1d-9417-a3e975cea112 · outbound

This paper cites Calibrating uncertainty for semi-supervised crowd counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Calibrating uncertainty for semi-supervised crowd counting

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:18.905706Z

Source-reported events for the cited work

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Observation b4d0cee3-25d3-412c-bba4-625c6567c698 · outbound

This paper cites Semi- supervised crowd counting based on hard pseudo-labels.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Semi- supervised crowd counting based on hard pseudo-labels

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:18.762431Z

Source-reported events for the cited work

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Observation d8c5319e-d478-46cc-8bb1-79037834bb8d · outbound

This paper cites Csrnet: Di- lated convolutional neural networks for understanding the highly congested scenes.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Csrnet: Di- lated convolutional neural networks for understanding the highly congested scenes

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:18.605035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation df1b2c8a-6157-40b4-afde-926c6de53c6d · outbound

This paper cites An end-to-end transformer model for crowd localization.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting An end-to-end transformer model for crowd localization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:18.460752Z

Source-reported events for the cited work

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Observation 24b363e0-13ae-45c9-91fa-dfc15a83731b · outbound

This paper cites Direct measure matching for crowd counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Direct measure matching for crowd counting

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:18.311530Z

Source-reported events for the cited work

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Observation cc866c13-121c-4847-9f4b-eb5faa44058d · outbound

This paper cites Semi-supervised crowd counting via density agency.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Semi-supervised crowd counting via density agency

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:18.138424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 89d81023-969c-4bbd-9fc6-ac1485db7c6c · outbound

This paper cites Boosting crowd counting via multifaceted attention.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Boosting crowd counting via multifaceted attention

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:18.001565Z

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Observation de31bc4a-ae02-4adb-8368-86282699b766 · outbound

This paper cites Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:24:06.547982Z

Source-reported events for the cited work

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Observation 2c04b7f4-c67f-414e-814e-400e03de6ff3 · outbound

This paper cites Optimal transport mini- mization: Crowd localization on density maps for semi- supervised counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Optimal transport mini- mization: Crowd localization on density maps for semi- supervised counting

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:17.858254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fcf7f7b8-20f9-4a14-8a82-978805329579 · outbound

This paper cites A fixed-point approach to unified prompt-based counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting A fixed-point approach to unified prompt-based counting

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:17.668404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 64fcfed0-be78-409f-aec5-b64173045900 · outbound

This paper cites Proximal mapping loss: Understanding loss functions in crowd counting & lo- calization.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Proximal mapping loss: Understanding loss functions in crowd counting & lo- calization

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:17.526033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e62eba8d-26fc-44b5-885c-8e2200f98cb3 · outbound

This paper cites Learning to detect anomaly events in crowd scenes from synthetic data.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Learning to detect anomaly events in crowd scenes from synthetic data

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:17.319827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c36bc91b-df1d-4216-a232-ffe8d5beb4da · outbound

This paper cites Scale- prior deformable convolution for exemplar-guided class- agnostic counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Scale- prior deformable convolution for exemplar-guided class- agnostic counting

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:17.129523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 62e37b4a-b523-462e-9ae2-55d401e1d754 · outbound

This paper cites Point-query quadtree for crowd counting, localization, and more.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Point-query quadtree for crowd counting, localization, and more

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:16.881791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7d925453-0b9d-4085-8023-15c23fb3aa39 · outbound

This paper cites Leveraging unlabeled data for crowd counting by learning to rank.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Leveraging unlabeled data for crowd counting by learning to rank

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:16.675940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f249e0d-7755-4643-9f5e-1e8d851a6b6c · outbound

This paper cites Exploiting unlabeled data in cnns by self-supervised learning to rank.IEEE transactions on pattern analysis and machine intelligence, 41(8):1862–1878, 2019.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Exploiting unlabeled data in cnns by self-supervised learning to rank.IEEE transactions on pattern analysis and machine intelligence, 41(8):1862–1878, 2019

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:16.491529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5e6447f1-5693-4239-8572-bb64ec14817e · outbound

This paper cites Semi-supervised crowd counting via self-training on surrogate tasks.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Semi-supervised crowd counting via self-training on surrogate tasks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:16.302525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d1128e9d-7163-4ac9-8243-3c0a02b82296 · outbound

This paper cites Towards unsupervised crowd counting via regression-detection bi-knowledge transfer.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Towards unsupervised crowd counting via regression-detection bi-knowledge transfer

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:16.054098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 71badb55-bd3d-49f7-8b13-70d85b2768b5 · outbound

This paper cites Semi-supervised crowd counting via multi-task pseudo-label self-correction strategy.IEEE Transactions on Circuits and Systems for Video Technology, 2024.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Semi-supervised crowd counting via multi-task pseudo-label self-correction strategy.IEEE Transactions on Circuits and Systems for Video Technology, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:15.844689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4318dd8a-72e2-4c1e-8ce0-595b892a901e · outbound

This paper cites Counting people crossing a line using integer programming and local features.IEEE Transactions on Circuits and Systems for Video Technology, 26(10):1955–1969, 2015.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Counting people crossing a line using integer programming and local features.IEEE Transactions on Circuits and Systems for Video Technology, 26(10):1955–1969, 2015

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:15.614361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3e40c625-4b18-4fad-a9de-ec4c4ad66a69 · outbound

This paper cites Bayesian loss for crowd count estimation with point supervi- sion.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Bayesian loss for crowd count estimation with point supervi- sion

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:15.416617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a8f5d83f-45bb-4291-a2f0-ccf49a84eff1 · outbound

This paper cites Learning to count via unbalanced optimal transport.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Learning to count via unbalanced optimal transport

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:15.233831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1e99eb81-65ca-4185-b736-3478c58ccb00 · outbound

This paper cites Domain generalization via gradient surgery.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Domain generalization via gradient surgery

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:14.996196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9416dc34-445d-4b7e-bf4d-dec0afed661e · outbound

This paper cites De- tection and tracking of groups in crowd.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting De- tection and tracking of groups in crowd

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:14.748826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 742c468d-5bd3-46df-a035-31dd4e48f788 · outbound

This paper cites Spa- tial uncertainty-aware semi-supervised crowd counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Spa- tial uncertainty-aware semi-supervised crowd counting

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:14.599891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f11e1749-ff6d-45d8-96c3-25b8c4a055ba · outbound

This paper cites Single domain general- ization for crowd counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Single domain general- ization for crowd counting

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:14.479461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 53c4f9ea-390f-42ad-a5cc-7abad84866f4 · outbound

This paper cites Ablation-cam: Visual explanations for deep convolutional network via gradient- free localization.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Ablation-cam: Visual explanations for deep convolutional network via gradient- free localization

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:14.170448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9fd616e0-f37a-4b83-9e1f-4c1d899671db · outbound

This paper cites Learning to count everything.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Learning to count everything

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:13.964601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2976a1db-6423-468a-833a-0e8d0c2e3a14 · outbound

This paper cites Tracking-by-counting: Using network flows on crowd density maps for tracking multiple targets.IEEE Transactions on Image Processing, 30:1439– 1452, 2020.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Tracking-by-counting: Using network flows on crowd density maps for tracking multiple targets.IEEE Transactions on Image Processing, 30:1439– 1452, 2020

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:13.715010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2584ba7c-ad40-4101-91be-e29d87a88328 · outbound

This paper cites Crowd counting and indi- vidual localization using pseudo square label.IEEE Access,.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Crowd counting and indi- vidual localization using pseudo square label.IEEE Access,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:13.404585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4e12aa9e-01e8-4edb-9daa-966bcbedfb18 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:02.597856Z

Source-reported events for the cited work

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Observation 2dc8a2d1-c6ab-47e8-95d8-26d198ae9bd8 · outbound

This paper cites Training- free object counting with prompts.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Training- free object counting with prompts

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:13.063815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d72d7b8c-0bc0-4188-b049-179ebcee32e4 · outbound

This paper cites Crowd counting in the frequency domain.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Crowd counting in the frequency domain

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:12.908785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 27eb7e5e-7c11-4727-9b61-09e68fda3028 · outbound

This paper cites Crowd counting in the frequency domain.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Crowd counting in the frequency domain

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:12.777716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b0bf71e9-291a-43e0-b7e9-b4c815aa64a9 · outbound

This paper cites Generalized char- acteristic function loss for crowd analysis in the frequency domain.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 2023.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Generalized char- acteristic function loss for crowd analysis in the frequency domain.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 2023

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:12.634932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 375ecae0-84e6-4f5c-aa6c-931b41f721a8 · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:03.059830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation addfdeee-e5c1-4cfe-9700-a9f4073b7ffc · outbound

This paper cites Learning to count in the crowd from limited labeled data.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Learning to count in the crowd from limited labeled data

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:12.474361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9e4b9b3e-4983-4918-9189-22d6e2c099c7 · outbound

This paper cites Jhu-crowd++: Large-scale crowd counting dataset and a benchmark method.IEEE transactions on pattern analysis and machine intelligence, 44(5):2594–2609, 2020.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Jhu-crowd++: Large-scale crowd counting dataset and a benchmark method.IEEE transactions on pattern analysis and machine intelligence, 44(5):2594–2609, 2020

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:12.328856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 63e253f9-2ce9-4e72-8d13-197647a4d22d · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:12.095004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 74f79154-bed7-44d3-8d70-fc91327315a9 · outbound

This paper cites Rethinking counting and localization in crowds: A purely point-based framework.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Rethinking counting and localization in crowds: A purely point-based framework

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:11.885822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8ccf5e90-5ec7-4988-8e9d-752d044d7ece · outbound

This paper cites Riedmiller.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Riedmiller

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:11.649668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 47b02ab4-d8a9-491f-a8d6-0cfa12030a14 · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30, 2017.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30, 2017

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:11.307081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3430701b-f1aa-4fdc-8e64-8ba5bfdbc240 · outbound

This paper cites Kernel- based density map generation for dense object counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Kernel- based density map generation for dense object counting

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:11.099769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7861a3df-aeca-42f8-abed-95da41bba726 · outbound

This paper cites A generalized loss function for crowd counting and localization.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting A generalized loss function for crowd counting and localization

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:10.891323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 62c1aaa4-e0fb-44a4-a02e-ae545e6ca790 · outbound

This paper cites Robust zero-shot crowd counting and localization with adaptive res- olution sam.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Robust zero-shot crowd counting and localization with adaptive res- olution sam

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:10.659047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 09a2a885-c9f8-4d5c-8eee-2a8ec9400058 · outbound

This paper cites Distribution matching for crowd counting.Ad- vances in neural information processing systems, 33:1595– 1607, 2020.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Distribution matching for crowd counting.Ad- vances in neural information processing systems, 33:1595– 1607, 2020

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:10.454487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4cfefb73-c01a-422b-ab37-2c0f0b19527f · outbound

This paper cites Score-cam: Score-weighted visual explanations for convolutional neural networks.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Score-cam: Score-weighted visual explanations for convolutional neural networks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:10.307455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dc3c4849-85c0-479b-b061-a8dfc4c44bbe · outbound

This paper cites Learning from synthetic data for crowd counting in the wild.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Learning from synthetic data for crowd counting in the wild

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:10.107788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9a557c0a-c694-4814-8155-45851ee1f379 · outbound

This paper cites Nwpu- crowd: A large-scale benchmark for crowd counting and lo- calization.IEEE transactions on pattern analysis and ma- chine intelligence, 43(6):2141–2149, 2020.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Nwpu- crowd: A large-scale benchmark for crowd counting and lo- calization.IEEE transactions on pattern analysis and ma- chine intelligence, 43(6):2141–2149, 2020

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.869998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ef16ee5d-2fe9-4c79-92e2-976d9a94cabb · outbound

This paper cites Density- aware curriculum learning for crowd counting.IEEE Trans- actions on Cybernetics, 52(6):4675–4687, 2020.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Density- aware curriculum learning for crowd counting.IEEE Trans- actions on Cybernetics, 52(6):4675–4687, 2020

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.662216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2a6ca7b1-bafe-48ee-ba65-173eb8c8bcfd · outbound

This paper cites Density- aware curriculum learning for crowd counting.IEEE Trans- actions on Cybernetics, 52(6):4675–4687, 2020.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Density- aware curriculum learning for crowd counting.IEEE Trans- actions on Cybernetics, 52(6):4675–4687, 2020

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.487140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b9324408-735d-4b1c-8c5e-09f466d2d12f · outbound

This paper cites Pixel-wise crowd understanding via synthetic data.International Jour- nal of Computer Vision, 129(1):225–245, 2021.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Pixel-wise crowd understanding via synthetic data.International Jour- nal of Computer Vision, 129(1):225–245, 2021

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.360355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 081e9bc0-c382-4c21-98f9-79cb775aa8da · outbound

This paper cites Dynamic mo- mentum adaptation for zero-shot cross-domain crowd count- ing.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Dynamic mo- mentum adaptation for zero-shot cross-domain crowd count- ing

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.268713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 49c7ba1b-533c-411c-9575-fc4c55411436 · outbound

This paper cites Zero-shot object counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Zero-shot object counting

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.121334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 589c601b-fb4f-4bce-a1aa-3ebb24d2f15f · outbound

This paper cites Cross-view cross-scene multi-view crowd counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Cross-view cross-scene multi-view crowd counting

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:09.003969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2af1b831-13a7-4b27-8471-03d9108f50ca · outbound

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

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Single-image crowd counting via multi-column convolutional neural network

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.867720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8114dffc-8aa0-4d06-a28f-0f46601b9b35 · outbound

This paper cites an unresolved cited work.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:24:08.727017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eb9d1cf0-7fd8-4a85-904d-c59ba5328977 · outbound

This paper cites Crowd anomaly event de- tection in surveillance video based on the evolution of the spatial position relationship feature.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Crowd anomaly event de- tection in surveillance video based on the evolution of the spatial position relationship feature

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.571310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 66bdc0cb-4392-4714-bc33-dcef3e8560b6 · outbound

This paper cites Gradient-based instance-specific visual explanations for ob- ject specification and object discrimination.IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 2024.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Gradient-based instance-specific visual explanations for ob- ject specification and object discrimination.IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 2024

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.442903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5eb39c94-c5ec-46c2-99e4-33294907d3fa · outbound

This paper cites Gradient-based visual explanation for transformer-based clip.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Gradient-based visual explanation for transformer-based clip

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.315971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0e36ea28-5668-4745-ac27-ee40297d7f26 · outbound

This paper cites Learning deep features for discrimina- tive localization.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Learning deep features for discrimina- tive localization

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T13:24:05.679648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ee676c8-b3ba-4c22-a8d9-0d9c79742a65 · outbound

This paper cites Fine-grained fragment diffusion for cross domain crowd counting.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Fine-grained fragment diffusion for cross domain crowd counting

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.148695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fecbc342-cee6-43bf-9fc5-561c8f5ebc17 · outbound

This paper cites Find gold in sand: Fine-grained similarity min- ing for domain-adaptive crowd counting.IEEE Transactions on Multimedia, 2023.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Find gold in sand: Fine-grained similarity min- ing for domain-adaptive crowd counting.IEEE Transactions on Multimedia, 2023

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:08.010952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0bff3fae-00e3-42d8-b256-36459664fcda · outbound

This paper cites 1 and Algo.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting 1 and Algo

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:07.847056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 56410653-20c7-4a4e-b692-88ff9c2d3a30 · outbound

This paper cites For labeled images, we apply horizontal flips to each cropped sample with a probability of 0.5 and randomly resize the images with a scale factor be- tween 0.7 and 1.3.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting For labeled images, we apply horizontal flips to each cropped sample with a probability of 0.5 and randomly resize the images with a scale factor be- tween 0.7 and 1.3

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:07.673453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3558320e-2510-4b21-9dd0-e60653c8c92a · outbound

This paper cites We present the empirical results in Table 3 to demonstrate its advantage.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting We present the empirical results in Table 3 to demonstrate its advantage

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:07.482057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e7e934c1-2436-44a6-a031-9cf04e3770bc · outbound

This paper cites Comparison of counting losses (100% Label Pct.) since the second term for the background part is set to 0, as shown in (9).

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Comparison of counting losses (100% Label Pct.) since the second term for the background part is set to 0, as shown in (9)

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:07.267669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d8dc1f0b-140b-4f9b-a2f2-baef22784cb8 · outbound

This paper cites 2:Related Works.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting 2:Related Works

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:07.060783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b49ffb20-16b1-4512-9edb-5e2b05968a3b · outbound

This paper cites Pseudo-Labels In Fig.

Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting Pseudo-Labels In Fig

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:24:06.879757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers