Paper Citation Record · LEDGER

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era

As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2607.22068.

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

pith.paper-citation-record.v1
2607.22068 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T05:59:06.662036Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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verified fuzzy0
unresolved31
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External citation measurements

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Outbound references

Observation 18689708-3c94-433e-b67d-421e8629ec2b · outbound

This paper cites VERI-Wild: A large dataset and a new method for vehicle re-identification in the wild,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era VERI-Wild: A large dataset and a new method for vehicle re-identification in the wild,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:03.427943Z

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Observation a7efd0da-5a0d-480c-92bb-9a25b889b928 · outbound

This paper cites A deep learning-based approach to progressive vehicle re-identification for urban surveillance,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era A deep learning-based approach to progressive vehicle re-identification for urban surveillance,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:03.468979Z

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Observation bc5b2566-4b29-417d-88ad-59c87a3c3423 · outbound

This paper cites A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:03.515543Z

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Observation 25a60418-cdf6-4940-9fee-4e82425a97f4 · outbound

This paper cites Learning discrimi- native features with multiple granularities for person re-identification,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Learning discrimi- native features with multiple granularities for person re-identification,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:03.576373Z

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Observation 67200b2c-5c3e-4d1c-8461-817f333778a5 · outbound

This paper cites Strength in diversity: Multi- branch representation learning for vehicle re-identification,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Strength in diversity: Multi- branch representation learning for vehicle re-identification,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:03.730600Z

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Observation e702086d-8e4e-426d-9479-cde2ca7b5f62 · outbound

This paper cites TransReID: Transformer-based object re-identification,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era TransReID: Transformer-based object re-identification,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:03.834968Z

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Unavailable: canonical work link unavailable.

Observation e16c8a04-e0ae-4b5f-8caa-7210b8397da2 · outbound

This paper cites Unity is strength: Unifying convolutional and transformeral features for better person re- identification,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Unity is strength: Unifying convolutional and transformeral features for better person re- identification,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:03.957954Z

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Observation a7f94cf5-2370-4a7a-9ca4-379d2ea99658 · outbound

This paper cites DINOv3.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era DINOv3

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:04.082116Z

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Observation 687de33f-87bb-443b-8ea4-c083566d9a55 · outbound

This paper cites CLIP-ReID: Exploiting vision-language model for image re-identification without concrete text labels,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era CLIP-ReID: Exploiting vision-language model for image re-identification without concrete text labels,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:04.153375Z

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Observation 2622816b-0afa-4306-98e0-ffbb21b0c5ba · outbound

This paper cites Fine- tuning can distort pretrained features and underperform out-of- distribution,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Fine- tuning can distort pretrained features and underperform out-of- distribution,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:04.233367Z

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Observation 367b6679-9411-45c6-ba27-e7d2e0ddf690 · outbound

This paper cites A ConvNet for the 2020s,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era A ConvNet for the 2020s,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:04.274376Z

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Observation 47ee7b20-74c7-4559-9226-6ad66a062c00 · outbound

This paper cites ResNet strikes back: An improved training procedure in timm.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era ResNet strikes back: An improved training procedure in timm

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:04.351841Z

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Observation 631fd7b2-67fd-4051-99bc-be2b039e705f · outbound

This paper cites Bag of tricks and a strong baseline for deep person re-identification,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Bag of tricks and a strong baseline for deep person re-identification,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:04.396809Z

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Observation 93e117e7-9fff-4377-b667-baee1ddb69cc · outbound

This paper cites Deep Ensembles: A Loss Landscape Perspective.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Deep Ensembles: A Loss Landscape Perspective

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:04.483142Z

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Observation 62a410da-2926-472f-8f07-bc160bb19504 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:04.567876Z

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Observation b30da2b7-a63b-406e-a10b-d64aae11263c · outbound

This paper cites Do vision transformers see like convolutional neural networks?.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Do vision transformers see like convolutional neural networks?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:04.730443Z

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Observation 08814eba-7779-4ea7-a0bd-e10b80f32a25 · outbound

This paper cites Similarity of neural network representations revisited,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Similarity of neural network representations revisited,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:04.893411Z

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Observation 95076ba1-bd51-4809-96c1-83f813da7831 · outbound

This paper cites Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:05.038525Z

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Observation 1846f4cc-f132-427e-9836-9471e6b65e25 · outbound

This paper cites Re-ranking person re- identification with k-reciprocal encoding,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Re-ranking person re- identification with k-reciprocal encoding,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:05.164180Z

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Observation 7c847d8b-ea6a-42ed-8346-7756d8d5a1ab · outbound

This paper cites Deep relative distance learning: Tell the difference between similar vehicles,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Deep relative distance learning: Tell the difference between similar vehicles,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:05.338287Z

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Observation 3dc23424-7647-4bbb-a903-a06e850e7ae5 · outbound

This paper cites CLIP-SENet: CLIP-based Semantic Enhancement Network for Vehicle Re-identification.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era CLIP-SENet: CLIP-based Semantic Enhancement Network for Vehicle Re-identification

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:05.496123Z

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Observation c087175b-3e6c-49c4-bec6-c2489ae49ebe · outbound

This paper cites BEiT: BERT pre-training of image transformers,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era BEiT: BERT pre-training of image transformers,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:05.667282Z

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Observation 2c8ce2e3-3831-4f1a-ba56-72d4b404b325 · outbound

This paper cites Circle loss: A unified perspective of pair similarity optimization,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Circle loss: A unified perspective of pair similarity optimization,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:05.835356Z

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Observation e0e13681-7665-4aed-be3a-418d941b6ac1 · outbound

This paper cites A discriminative feature learning approach for deep face recognition,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era A discriminative feature learning approach for deep face recognition,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:05.911542Z

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Observation 70e905eb-3eba-4b35-8307-8596343526ba · outbound

This paper cites In Defense of the Triplet Loss for Person Re-Identification.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era In Defense of the Triplet Loss for Person Re-Identification

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:06.042177Z

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Observation d91b7075-60ea-4b4a-b26d-77160c3b5792 · outbound

This paper cites Visualizing and understanding convolu- tional networks,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Visualizing and understanding convolu- tional networks,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:06.133638Z

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Observation ec5c4401-a89c-4236-8d83-c06b353579a5 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:06.279987Z

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Observation 1a8c5024-679b-427d-9278-3fb15842e950 · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era LoRA: Low-rank adaptation of large language models,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:06.418063Z

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Observation ebb3b14c-dd65-4a7c-be0c-5ff29fef30ae · outbound

This paper cites Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:06.527171Z

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Observation ad879878-63ea-4c83-ad67-7fcc6d6ae9ab · outbound

This paper cites Fine-tuning CNN image retrieval with no human annotation,.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Fine-tuning CNN image retrieval with no human annotation,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:06.579780Z

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Observation 1929b805-0553-4b16-a7c0-5e8bbe74303a · outbound

This paper cites Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline),.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline),

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:06.662036Z

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Pith citing papers

No inbound Pith citation observations are available.