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Paper Citation Record · LEDGER

Hierarchical and Multimodal Data for Daily Activity Understanding

As of 20 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 3 inbound Pith citation observations for arXiv:2504.17696.

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

pith.paper-citation-record.v1
2504.17696 v4

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:37:33.021604Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:29:37.755186Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T11:26:04.466619Z

Reference resolution

32 of 32 outbound references displayed

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  • verified fuzzy10
  • unresolved14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6147b2cf-12f9-4ac4-93ee-a535fa20c82d · outbound

This paper cites doi: 10.3390/s21186037.

Hierarchical and Multimodal Data for Daily Activity Understanding doi: 10.3390/s21186037

Reference 2

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Observation 23ef1ffa-71ea-413e-b959-4dba53680d5f · outbound

This paper cites doi: 10.3390/s23052715.

Hierarchical and Multimodal Data for Daily Activity Understanding doi: 10.3390/s23052715

Reference 4

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Observation 06412fbb-c608-40a4-af5f-bbf8a74002ec · outbound

This paper cites The choice of the number of subsequences was made based on the original data length and sampling rate.

Hierarchical and Multimodal Data for Daily Activity Understanding The choice of the number of subsequences was made based on the original data length and sampling rate

Reference 5

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Source-reported events for the cited work

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Observation d2667a08-8d5b-4720-8105-d272425bf016 · outbound

This paper cites Qiuhong Ke, Mario Fritz, and Bernt Schiele.

Hierarchical and Multimodal Data for Daily Activity Understanding Qiuhong Ke, Mario Fritz, and Bernt Schiele

Reference 8

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Source-reported events for the cited work

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Observation 106b6676-18bb-4964-bb6b-d638f0f42427 · outbound

This paper cites Seulgi Kim, Ghazal Kaviani, Mohit Prabhushankar, and Ghassan AlRegib.

Hierarchical and Multimodal Data for Daily Activity Understanding Seulgi Kim, Ghazal Kaviani, Mohit Prabhushankar, and Ghassan AlRegib

Reference 9

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Observation 4ee33d0c-bf9f-4c72-96c5-f7787cb90157 · outbound

This paper cites Probing Fine-Grained Action Understanding and Cross-View Generalization of Foundation Models.

Hierarchical and Multimodal Data for Daily Activity Understanding Probing Fine-Grained Action Understanding and Cross-View Generalization of Foundation Models

Reference 12

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Observation 1117e854-1dbb-486d-a4e4-6d811d5d2aa1 · outbound

This paper cites Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks.

Hierarchical and Multimodal Data for Daily Activity Understanding Counterfactual Gradients-based Quantification of Prediction Trust in Neural Networks

Reference 13

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Observation 5dd79d80-c347-45af-aa26-3bbb92d4c9f0 · outbound

This paper cites Vladimir M Sloutsky.

Hierarchical and Multimodal Data for Daily Activity Understanding Vladimir M Sloutsky

Reference 15

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Observation 41c25e9f-8790-459d-9fce-75f85921ef9f · outbound

This paper cites Combining embedded accelerometers with com- puter vision for recognizing food preparation activities.

Hierarchical and Multimodal Data for Daily Activity Understanding Combining embedded accelerometers with com- puter vision for recognizing food preparation activities

Reference 16

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Source-reported events for the cited work

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Observation e21c090b-b7fc-42d1-9ecd-81cf3590d82f · outbound

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Hierarchical and Multimodal Data for Daily Activity Understanding Unresolved cited work

Reference 17

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Observation cb9ba3c9-cb17-40dd-b675-48b8010fbc1a · outbound

This paper cites Keze Wang, Xiaolong Wang, Liang Lin, Meng Wang, and Wangmeng Zuo.

Hierarchical and Multimodal Data for Daily Activity Understanding Keze Wang, Xiaolong Wang, Liang Lin, Meng Wang, and Wangmeng Zuo

Reference 19

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Observation 64031f18-dd1a-4fff-a57e-05f6c029e77e · outbound

This paper cites Zhiguang Wang and Tim Oates.

Hierarchical and Multimodal Data for Daily Activity Understanding Zhiguang Wang and Tim Oates

Reference 20

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Source-reported events for the cited work

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Observation 8f738fbe-d7f1-4a56-a685-d460fbdb16f7 · outbound

This paper cites Input Sequence Length.

Hierarchical and Multimodal Data for Daily Activity Understanding Input Sequence Length

Reference 21

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Source-reported events for the cited work

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Observation 27cc8351-6261-4442-8ae9-680dee3061cd · outbound

This paper cites Jinglin Xu, Guohao Zhao, Sibo Yin, Wenhao Zhou, and Yuxin Peng.

Hierarchical and Multimodal Data for Daily Activity Understanding Jinglin Xu, Guohao Zhao, Sibo Yin, Wenhao Zhou, and Yuxin Peng

Reference 22

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Observation c525d550-4b3a-4c6c-9a99-9769a3ff6a42 · outbound

This paper cites Subject Invariant Contrastive Learning for Human Activity Recognition.

Hierarchical and Multimodal Data for Daily Activity Understanding Subject Invariant Contrastive Learning for Human Activity Recognition

Reference 23

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Observation df65c05d-8a13-4443-8ef4-3c131831edfa · outbound

This paper cites The license associated with our dataset is a Creative Commons International 4 license.

Hierarchical and Multimodal Data for Daily Activity Understanding The license associated with our dataset is a Creative Commons International 4 license

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 6686f059-860d-4691-ba99-1ffda50e9d4b · outbound

This paper cites The optimal configuration is then passed as parameters during recording.

Hierarchical and Multimodal Data for Daily Activity Understanding The optimal configuration is then passed as parameters during recording

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation c520f011-d7d9-416b-bf7b-7d0c44ddcd80 · outbound

This paper cites Calibration of this camera involves adjusting the RGB and depth settings separately and then aligning the outputs of the two sensors to achieve higher quality depth and RGB frames.

Hierarchical and Multimodal Data for Daily Activity Understanding Calibration of this camera involves adjusting the RGB and depth settings separately and then aligning the outputs of the two sensors to achieve higher quality depth and RGB frames

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 177e3b9e-dbb6-4169-86dc-1d85f1f697fb · outbound

This paper cites The results for different architectures can be found in Appendix G.

Hierarchical and Multimodal Data for Daily Activity Understanding The results for different architectures can be found in Appendix G

Reference 28

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 06b51e6b-6137-4fbe-ac6f-af62c19a1235 · outbound

This paper cites an unresolved cited work.

Hierarchical and Multimodal Data for Daily Activity Understanding Unresolved cited work

Reference 30

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Observation 3a390f9d-3293-4b17-ad32-4ffc2ddb323a · outbound

This paper cites As the dataset was recorded at 15 frames per second (fps), we parsed the data by sampling one frame every 15 frames.

Hierarchical and Multimodal Data for Daily Activity Understanding As the dataset was recorded at 15 frames per second (fps), we parsed the data by sampling one frame every 15 frames

Reference 32

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Observation ba8cbda5-138e-439d-9819-f5668510aca4 · outbound

This paper cites doi: 10.1145/219717.219748.

Hierarchical and Multimodal Data for Daily Activity Understanding doi: 10.1145/219717.219748

Reference 1995

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Observation cfbfdba6-b3f5-4ffc-8f4a-e201c7af9ab2 · outbound

This paper cites A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition.

Hierarchical and Multimodal Data for Daily Activity Understanding A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition

Reference 2016

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Observation cfbc89d7-7f30-4c00-a33c-b4d70d829f87 · outbound

This paper cites Xia, C.C.

Hierarchical and Multimodal Data for Daily Activity Understanding Xia, C.C

Reference 2017

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Observation 8ec202fe-a81b-438e-9058-8e62a64764d2 · outbound

This paper cites doi: 10.1109/TI TS.2019.2931429.

Hierarchical and Multimodal Data for Daily Activity Understanding doi: 10.1109/TI TS.2019.2931429

Reference 2019

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Observation dbf58441-ca70-421d-8c86-21a0fe522645 · outbound

This paper cites Coarse to Fine Multi-Resolution Temporal Convolutional Network.

Hierarchical and Multimodal Data for Daily Activity Understanding Coarse to Fine Multi-Resolution Temporal Convolutional Network

Reference 2020

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Observation 7a293832-adfa-41c2-b986-c23615a634ca · outbound

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Hierarchical and Multimodal Data for Daily Activity Understanding Unresolved cited work

Reference 2021

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Observation 885f9764-d48a-43cd-91e4-7e94b3643245 · outbound

This paper cites Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives.

Hierarchical and Multimodal Data for Daily Activity Understanding Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

Reference 2022

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This paper cites SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes.

Hierarchical and Multimodal Data for Daily Activity Understanding SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes

Reference 2023

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This paper cites URLhttps://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.13680.

Hierarchical and Multimodal Data for Daily Activity Understanding URLhttps://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.13680

Reference 2024

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verified exact
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Source-reported events for the cited work

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Observation 73695296-e7f3-4609-b8f0-a5bddc72d645 · outbound

This paper cites Towards automatic learning of procedures from web instructional videos.Proceedings of the AAAI Conference on Artificial Intelligence, 32 (1), Apr.

Hierarchical and Multimodal Data for Daily Activity Understanding Towards automatic learning of procedures from web instructional videos.Proceedings of the AAAI Conference on Artificial Intelligence, 32 (1), Apr

Reference 2025

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Observation 073f6414-1dc5-46dd-9baa-f45021460002 · outbound

This paper cites Clinically labeled contrastive learning for oct biomarker classification.

Hierarchical and Multimodal Data for Daily Activity Understanding Clinically labeled contrastive learning for oct biomarker classification

Reference 2026

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Source-reported events for the cited work

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Pith citing papers

Observation 9f23d891-1f13-48f7-906f-f5c48e910bb5 · inbound

Multi-level and Multi-modal Action Anticipation cites this paper.

Multi-level and Multi-modal Action Anticipation Hierarchical and Multimodal Data for Daily Activity Understanding

Reference 19

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Observation 70d4b906-47d1-46fd-9a2c-5f875efbc746 · inbound

Information Router for Mitigating Modality Dominance in Vision-Language Models cites this paper.

Information Router for Mitigating Modality Dominance in Vision-Language Models Hierarchical and Multimodal Data for Daily Activity Understanding

Reference 20

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Observation c9fadf8c-f32b-4f56-b91a-fc59a2b151ae · inbound

MER-DG: Modality-Entropy Regularization for Multimodal Domain Generalization cites this paper.

MER-DG: Modality-Entropy Regularization for Multimodal Domain Generalization Hierarchical and Multimodal Data for Daily Activity Understanding

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-10T14:53:57.003351Z digest=sha256:b0dfe8b82300006f95b27394f56529303ac9ed5c1ee5649ec23c74641667071c