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Paper Citation Record · LEDGER

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge

As of 6 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2604.19054.

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

pith.paper-citation-record.v1
2604.19054 v3

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T15:52:17.137712Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

22 of 22 outbound references displayed

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External citation measurements

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Outbound references

Observation b4039f91-d555-4a00-9e02-480037247f8a · outbound

This paper cites Rebooting computing and low-power image recognition challenge,.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Rebooting computing and low-power image recognition challenge,

Reference 1

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Observation 7ab2895f-c375-4fec-ad41-db4c6008f0a3 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Imagenet large scale visual recognition challenge,

Reference 2

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Observation 1b283c08-a4f0-4270-a5a5-8855b7052acf · outbound

This paper cites Microsoft COCO: Common Objects in Context.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Microsoft COCO: Common Objects in Context

Reference 3

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Observation 23611d8c-15e8-4008-9f6f-1d4c0bc7d1cb · outbound

This paper cites The sixth visual object tracking vot2018 challenge results,.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge The sixth visual object tracking vot2018 challenge results,

Reference 4

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Observation 216c5f63-2164-44d4-b668-f4cdbcd2b934 · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge The 2017 DAVIS Challenge on Video Object Segmentation

Reference 5

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Observation 3bcb9c0d-3eaa-4391-bdcf-469215d1fd1d · outbound

This paper cites Neurips competition track,.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Neurips competition track,

Reference 6

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Observation aeec5e3a-5cff-4f5e-9a78-cba4985ffe23 · outbound

This paper cites Low-power image recognition chal- lenge,.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Low-power image recognition chal- lenge,

Reference 7

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Observation 0605d94e-0e30-42ba-9451-c453e529fe83 · outbound

This paper cites Special session: 2018 low-power image recognition challenge and beyond,.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Special session: 2018 low-power image recognition challenge and beyond,

Reference 8

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Observation 86ec78cc-327f-4d80-a184-4114e6633b6f · outbound

This paper cites Low-power computer vision: Status, challenges, and opportunities,.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Low-power computer vision: Status, challenges, and opportunities,

Reference 9

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Observation 6b2bcc08-88d8-4b8c-b3c8-b7a9f163282c · outbound

This paper cites The 2020 low-power computer vision chal- lenge,.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge The 2020 low-power computer vision chal- lenge,

Reference 10

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source=pdf_text observed=2026-08-02T15:52:17.107609Z digest=sha256:7d20ff855fbdf9714a94c7d6f53c356e78fb57db571e3504b3692acaece2a4c8

Observation c1af261e-9246-4e65-bbe2-a58b922098db · outbound

This paper cites Evolution of winning solutions in the 2021 low-power computer vision challenge,.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Evolution of winning solutions in the 2021 low-power computer vision challenge,

Reference 11

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Observation 79bab0f0-0bfb-4169-9d41-e0b070ae4a8a · outbound

This paper cites 2023 Low-Power Computer Vision Challenge (LPCVC) Summary.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge 2023 Low-Power Computer Vision Challenge (LPCVC) Summary

Reference 12

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Observation 4b4731a1-352a-4c21-ac31-652971be43ac · outbound

This paper cites MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge MobileNetV2: Inverted Residuals and Linear Bottlenecks

Reference 13

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Observation 54107eee-7e1f-46a8-b1c2-361ee01c6721 · outbound

This paper cites Generalized Decoding for Pixel, Image, and Language.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Generalized Decoding for Pixel, Image, and Language

Reference 14

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Observation 89f54725-0cf8-4dec-bebd-f1ecf0ce57e9 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Learning Transferable Visual Models From Natural Language Supervision

Reference 15

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Observation a8a7bc1f-1030-4133-83aa-a8b3f0c5a404 · outbound

This paper cites Depth Anything V2.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Depth Anything V2

Reference 16

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Observation 3ed4bd43-8701-43c1-9180-4408708fb44f · outbound

This paper cites MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training

Reference 17

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Observation d6b95cfa-2532-4e75-a96f-51ed66418ba6 · outbound

This paper cites Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Reference 18

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Observation 8cf01795-314c-4512-9774-fa66e59401e9 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge LoRA: Low-Rank Adaptation of Large Language Models

Reference 19

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Observation ad1c8c1f-a4c0-4c53-ab48-5cb94a9cf281 · outbound

This paper cites Visual prompt tuning,.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Visual prompt tuning,

Reference 20

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Observation ebc31b22-631b-4e64-a1f9-b53be4473547 · outbound

This paper cites Available: https://arxiv.org/abs/1409.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Available: https://arxiv.org/abs/1409

Reference 2015

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Observation e98f4a76-61b7-475e-adbf-6591f990de19 · outbound

This paper cites Available: https://arxiv.org/abs/2203.

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge Available: https://arxiv.org/abs/2203

Reference 2022

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Pith citing papers

No inbound Pith citation observations are available.