Single Domain Generalization for Few-Shot Counting via Universal Representation Matching | Paper Citation Record

Observation 378e718f-1df4-44f0-a767-836835adc2ec · outbound

This paper cites Gpt-4 technical report.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Gpt-4 technical report

Reference 1

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

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Open-world Text-specified Object Counting

Reference 2

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This paper cites Introducing the next generation of claude.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Introducing the next generation of claude

Reference 3

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This paper cites Explicit invariant feature induced cross-domain crowd counting.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Explicit invariant feature induced cross-domain crowd counting

Reference 4

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This paper cites Dearkd: Data-efficient early knowledge distillation for vision transformers.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12042–12052, 2022.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Dearkd: Data-efficient early knowledge distillation for vision transformers.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12042–12052, 2022

Reference 5

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This paper cites Open-vocabulary panoptic segmentation with embedding modulation.2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 1141–1150,.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Open-vocabulary panoptic segmentation with embedding modulation.2023 IEEE/CVF International Conference on Computer Vision (ICCV), pages 1141–1150,

Reference 6

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Observation a8032dc0-d311-4368-8d39-60d8600f89ec · outbound

This paper cites Transfer CLIP for Generalizable Image Denoising.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Transfer CLIP for Generalizable Image Denoising

Reference 7

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This paper cites Reproducible scaling laws for contrastive language-image learning.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2818–2829, 2022.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Reproducible scaling laws for contrastive language-image learning.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2818–2829, 2022

Reference 8

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Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Unresolved cited work

Reference 9

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This paper cites A low-shot object counting network with iterative proto- type adaptation.2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2022.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching A low-shot object counting network with iterative proto- type adaptation.2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2022

Reference 10

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Observation 53e49973-47ac-400a-81be-71695238e77a · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 11

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Observation bae1b661-378d-4d2e-bb40-64af3732c6a5 · outbound

This paper cites Domain-General Crowd Counting in Unseen Scenarios.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Domain-General Crowd Counting in Unseen Scenarios

Reference 12

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Observation ebf0d78e-b2ba-4da6-892a-35926e53af48 · outbound

This paper cites Domain- adaptive crowd counting via high-quality image translation and density reconstruction.IEEE Transactions on Neural Networks and Learning Systems, 34:4803–4815, 2019.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Domain- adaptive crowd counting via high-quality image translation and density reconstruction.IEEE Transactions on Neural Networks and Learning Systems, 34:4803–4815, 2019

Reference 13

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Observation 87d9aec6-20dc-4fc1-a503-88aaa16f4e42 · outbound

This paper cites Yu, Stephen J.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Yu, Stephen J

Reference 14

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Observation c158b206-40ac-48cf-a3d4-f979ab4fbda4 · outbound

This paper cites Girshick.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Girshick

Reference 15

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Observation 0888d7cb-5fb7-4592-84af-abe5cf9208c3 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Distilling the Knowledge in a Neural Network

Reference 16

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Observation 24f260c0-e003-4b56-9e36-56ed45b130db · outbound

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

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision

Reference 17

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This paper cites FROSTER: Frozen CLIP Is A Strong Teacher for Open-Vocabulary Action Recognition.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching FROSTER: Frozen CLIP Is A Strong Teacher for Open-Vocabulary Action Recognition

Reference 18

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This paper cites Point, segment and count: A gen- eralized framework for object counting.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17067–17076, 2023.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Point, segment and count: A gen- eralized framework for object counting.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17067–17076, 2023

Reference 19

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Observation e18552d2-66f0-4621-91b9-12c30b132df6 · outbound

This paper cites T-rex2: Towards generic object detec- tion via text-visual prompt synergy.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching T-rex2: Towards generic object detec- tion via text-visual prompt synergy

Reference 20

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

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Clip- count: Towards text-guided zero-shot object counting

Reference 21

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Observation 73dd514c-0579-46f6-97b9-e41f27c6de4f · outbound

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

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Vlcounter: Text-aware visual representation for zero- shot object counting

Reference 22

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This paper cites Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross B.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross B

Reference 23

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This paper cites An introduction to domain adaptation and transfer learning.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching An introduction to domain adaptation and transfer learning

Reference 24

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This paper cites ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference

Reference 25

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Observation 9584bd32-f3cd-4f3d-8ee6-a5112e1dbac6 · outbound

This paper cites What matters when building vision-language models?.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching What matters when building vision-language models?

Reference 26

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This paper cites Hospedales.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Hospedales

Reference 27

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This paper cites PromptKD: Unsupervised Prompt Distillation for Vision-Language Models.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching PromptKD: Unsupervised Prompt Distillation for Vision-Language Models

Reference 28

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This paper cites Locating and counting heads in crowds with a depth prior.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44:9056–9072, 2021.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Locating and counting heads in crowds with a depth prior.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44:9056–9072, 2021

Reference 29

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This paper cites Crowdclip: Unsupervised crowd counting via vision-language model.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2893–2903, 2023.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Crowdclip: Unsupervised crowd counting via vision-language model.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2893–2903, 2023

Reference 30

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Observation 6394ad35-4a3b-429b-b42f-daf4679ed9f4 · outbound

This paper cites Object Counting: You Only Need to Look at One.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Object Counting: You Only Need to Look at One

Reference 31

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Observation 31ab9e46-ddde-4580-ad21-b2aa6f9ae71f · outbound

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

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching CounTR: Transformer-based Generalised Visual Counting

Reference 32

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This paper cites Zhao, Qiu Qiang, and Pan Li.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Zhao, Qiu Qiang, and Pan Li

Reference 33

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Observation f8d1e90d-3aff-4aa9-8247-7b0db8e98fae · outbound

This paper cites Towards unsupervised crowd counting via regression-detection bi-knowledge trans- fer.Proceedings of the 28th ACM International Conference on Multimedia, 2020.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Towards unsupervised crowd counting via regression-detection bi-knowledge trans- fer.Proceedings of the 28th ACM International Conference on Multimedia, 2020

Reference 34

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This paper cites Milone, and Enzo Ferrante.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Milone, and Enzo Ferrante

Reference 35

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This paper cites Few-shot Object Counting and Detection.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Few-shot Object Counting and Detection

Reference 36

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Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Unresolved cited work

Reference 37

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This paper cites Teaching clip to count to ten.2023 IEEE/CVF International Conference on Com- puter Vision (ICCV), pages 3147–3157, 2023.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Teaching clip to count to ten.2023 IEEE/CVF International Conference on Com- puter Vision (ICCV), pages 3147–3157, 2023

Reference 38

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This paper cites Two at once: Enhancing learning and generalization capacities via ibn-net.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Two at once: Enhancing learning and generalization capacities via ibn-net

Reference 39

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Observation feb2ed15-c4f8-4ab3-9807-3783a57202ba · outbound

This paper cites Switchable whitening for deep representa- tion learning.2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 1863–1871, 2019.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Switchable whitening for deep representa- tion learning.2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 1863–1871, 2019

Reference 40

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Observation 69c4e663-df27-4869-9756-9fe1109e3761 · outbound

This paper cites DAVE -- A Detect-and-Verify Paradigm for Low-Shot Counting.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching DAVE -- A Detect-and-Verify Paradigm for Low-Shot Counting

Reference 41

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Observation 6ae0db6b-f904-460e-906b-1056aa5645d6 · outbound

This paper cites Single Domain Generalization for Crowd Counting.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Single Domain Generalization for Crowd Counting

Reference 42

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Observation 693ef177-168b-422f-ac9c-f2f22d86540f · outbound

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Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Unresolved cited work

Reference 43

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Observation 73de990b-fb56-4a55-a655-58f671e535db · outbound

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

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Learning transferable visual models from natural language supervision

Reference 44

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Observation 9aa3cbc0-d6be-48c2-b932-2d16003c6e45 · outbound

This paper cites Exemplar free class agnostic counting.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Exemplar free class agnostic counting

Reference 45

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Observation d6fe067c-3973-47f1-80f1-b3e0b3a26c0c · outbound

This paper cites Vicinal counting networks.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Vicinal counting networks

Reference 46

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Observation dfeaf7a7-97e5-4f2c-b370-4ae71ed6fd41 · outbound

This paper cites Learning to count everything.2021 IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR), pages 3393–3402, 2021.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Learning to count everything.2021 IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR), pages 3393–3402, 2021

Reference 47

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Observation 91ecc0a7-7b71-4e86-93a1-709e91e0adba · outbound

This paper cites Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection

Reference 48

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Observation 58851f01-2e76-4816-a732-9ae2e58ff1ce · outbound

This paper cites Girshick, and Jian Sun.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Girshick, and Jian Sun

Reference 49

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Observation a7878cde-e468-4379-b581-7753b016592b · outbound

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

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection

Reference 50

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Observation 2d083f45-7090-48ae-8b5f-8ce26cd2fdf6 · outbound

This paper cites Edadet: Open-vocabulary object detection using early dense alignment.2023 IEEE/CVF In- ternational Conference on Computer Vision (ICCV), pages 15678–15688, 2023.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Edadet: Open-vocabulary object detection using early dense alignment.2023 IEEE/CVF In- ternational Conference on Computer Vision (ICCV), pages 15678–15688, 2023

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:13.408826Z

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 62838792-2a46-4818-8c31-12db8fcde101 · outbound

This paper cites an unresolved cited work.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:59:13.142214Z

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 d338a1da-4925-48c5-bdcf-98e7551e82a5 · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T14:58:59.961044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5153ba1b-f85d-44ed-b89d-c4df2fe1cdd4 · outbound

This paper cites Training data-efficient image transformers & distillation through at- tention.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Training data-efficient image transformers & distillation through at- tention

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:12.853805Z

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 e4ee3f90-5d2c-41fe-9851-9446eca760b7 · outbound

This paper cites an unresolved cited work.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:59:12.569317Z

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 0d30cf03-8fa7-4be7-be26-f7e1d149f83b · outbound

This paper cites Adversarial discriminative domain adaptation.2017 IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR), pages 2962–2971, 2017.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Adversarial discriminative domain adaptation.2017 IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR), pages 2962–2971, 2017

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:12.292581Z

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 9999508b-015d-4159-a6aa-0432c72ccc8b · outbound

This paper cites an unresolved cited work.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:59:11.924383Z

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 10c713a5-d103-4f82-af38-8efff7d0871b · outbound

This paper cites Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:11.655778Z

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 88729914-9165-4a5d-aa3c-bb1ce05aeec3 · outbound

This paper cites Language-guided zero- shot object counting.2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pages 1–6,.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Language-guided zero- shot object counting.2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pages 1–6,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:11.363846Z

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 87c765cf-29a4-4d0e-b9ec-c206720b90a5 · outbound

This paper cites Learn- ing from synthetic data for crowd counting in the wild.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Learn- ing from synthetic data for crowd counting in the wild

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:11.110256Z

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 cc98c890-ac03-48f2-9d37-3fac878cdb02 · outbound

This paper cites Detection, track- ing, and counting meets drones in crowds: A benchmark.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Detection, track- ing, and counting meets drones in crowds: A benchmark

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:10.823985Z

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 8c1aff51-bbdc-4518-a0a2-55e57cd7f276 · outbound

This paper cites an unresolved cited work.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:59:10.626087Z

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 cb5ac2da-2183-43bf-8145-0d2545a0a924 · outbound

This paper cites Le, Vu Nguyen, Viresh Ranjan, and Dim- itris Samaras.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Le, Vu Nguyen, Viresh Ranjan, and Dim- itris Samaras

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:10.303777Z

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 ac29cfb5-f3d8-4c3a-9800-be1fb04dfcae · outbound

This paper cites Clip-kd: An empirical study of clip model distillation.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15952–15962, 2023.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Clip-kd: An empirical study of clip model distillation.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15952–15962, 2023

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:09.961050Z

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 fe71e008-89ed-4bdd-a544-e1532eab0368 · outbound

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

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Detclipv3: To- wards versatile generative open-vocabulary object detection

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:09.579117Z

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 60d0c62d-22b0-4b05-871f-e53526a87785 · outbound

This paper cites Lu, Lei Cui, and Xinyi Le.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Lu, Lei Cui, and Xinyi Le

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:09.240092Z

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 93c14773-15c3-40b8-944d-147816f3d03b · outbound

This paper cites Turning a clip model into a scene text de- tector.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6978–6988, 2023.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Turning a clip model into a scene text de- tector.2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6978–6988, 2023

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:08.896125Z

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 39192ab2-a585-4147-8095-4315da6ade27 · outbound

This paper cites A segmentation-based approach for polyp counting in the wild.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching A segmentation-based approach for polyp counting in the wild

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:08.539287Z

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 63b776a3-de0a-4ec3-a415-cb5673e84453 · outbound

This paper cites Sigmoid loss for language image pre-training.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Sigmoid loss for language image pre-training

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:08.284523Z

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 4e298632-a787-49fc-bd16-2dda7eb4f608 · outbound

This paper cites Single-image crowd counting via multi-column convolutional neural network.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 589–597, 2016.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Single-image crowd counting via multi-column convolutional neural network.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 589–597, 2016

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:07.988227Z

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 c38dcdf1-237c-439e-94e0-91c601db2e2e · outbound

This paper cites Why are Visually-Grounded Language Models Bad at Image Classification?.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Why are Visually-Grounded Language Models Bad at Image Classification?

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T14:59:02.227715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf394bad-f7ce-49e9-bdee-a23b0ba8e6ee · outbound

This paper cites Decoupled knowledge distillation.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11943–11952, 2022.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Decoupled knowledge distillation.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11943–11952, 2022

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:07.753908Z

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 1fadc784-d757-4d4e-b12a-1f0de82c3f67 · outbound

This paper cites Extract free dense labels from clip.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Extract free dense labels from clip

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:07.577046Z

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 743443c9-2aed-40df-bfa4-cae95158edd8 · outbound

This paper cites Domain generalization: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45:4396–4415, 2021.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Domain generalization: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45:4396–4415, 2021

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:07.400845Z

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 eafc735c-b372-4eae-91fd-20e88f6a8ad5 · outbound

This paper cites Fine-grained fragment diffusion for cross do- main crowd counting.Proceedings of the 30th ACM Inter- national Conference on Multimedia, 2022.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Fine-grained fragment diffusion for cross do- main crowd counting.Proceedings of the 30th ACM Inter- national Conference on Multimedia, 2022

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:07.191111Z

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 cf0c213c-99d8-4c0b-9bf2-de59ae79baa8 · outbound

This paper cites Daot: Domain- agnostically aligned optimal transport for domain-adaptive crowd counting.Proceedings of the 31st ACM International Conference on Multimedia, 2023.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Daot: Domain- agnostically aligned optimal transport for domain-adaptive crowd counting.Proceedings of the 31st ACM International Conference on Multimedia, 2023

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:06.843280Z

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 068849ab-ebdf-4f06-be54-138fcedee45e · outbound

This paper cites Zero-shot Object Counting with Good Exemplars.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Zero-shot Object Counting with Good Exemplars

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:59:03.523395Z

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 d36abe11-82db-4c6f-a107-3002194cf6fe · outbound

This paper cites URM learns universal knowledge through distillation only during the training phase, making it as efficient as other methods during inference.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching URM learns universal knowledge through distillation only during the training phase, making it as efficient as other methods during inference

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:06.187837Z

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 7437df3d-f7c8-4976-895d-6c015f18c115 · outbound

This paper cites an unresolved cited work.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Unresolved cited work

Reference 80

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T14:59:05.904083Z

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 1361f4a8-7f1d-4c49-b5c1-72fff57666d8 · outbound

This paper cites Then, we perform an ablation study on the prompts for language representa- tion in Table 12.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching Then, we perform an ablation study on the prompts for language representa- tion in Table 12

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:05.481096Z

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 24f9d8ea-f9d4-451d-bdc0-d546fcfe89cb · outbound

This paper cites We show that the method yields com- pelling open set segmentation results and is robust to data augmentation.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching We show that the method yields com- pelling open set segmentation results and is robust to data augmentation

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:05.219209Z

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 ca3c0169-fef2-49e0-a245-231cdff6205d · outbound

This paper cites 27.32 43.28 URM-V 26.54 42.82 Table 8.Comparison with generating visual prototypes by us- ing CLIP vision encoder directly.

Single Domain Generalization for Few-Shot Counting via Universal Representation Matching 27.32 43.28 URM-V 26.54 42.82 Table 8.Comparison with generating visual prototypes by us- ing CLIP vision encoder directly

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:59:06.560255Z

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