Paper Citation Record · LEDGER

MoExDA: Domain Adaptation for Edge-based Action Recognition

As of 7 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2508.02981.

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

pith.paper-citation-record.v1
2508.02981 v1

Coverage vector

measured 47 of 47 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation 2bee1ea6-989a-49b9-86df-d61a9ff919e5 · outbound

This paper cites Human action recognition and prediction: A survey,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Human action recognition and prediction: A survey,

Reference 1

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This paper cites Human action recognition and prediction: A survey,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Human action recognition and prediction: A survey,

Reference 2

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Observation 54556ce8-6ecd-4fb3-9a96-4958004a2158 · outbound

This paper cites Video ac- tion understanding,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Video ac- tion understanding,

Reference 3

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Observation c81c4902-4ebb-4fdd-a6c1-791034c1639d · outbound

This paper cites Can spatiotem- poral 3d cnns retrace the history of 2d cnns and im- agenet?.

MoExDA: Domain Adaptation for Edge-based Action Recognition Can spatiotem- poral 3d cnns retrace the history of 2d cnns and im- agenet?

Reference 4

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Observation 02de44b7-7b9e-4b24-82b2-d973366a378b · outbound

This paper cites Enabling detailed action recognition evaluation through video dataset augmentation,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Enabling detailed action recognition evaluation through video dataset augmentation,

Reference 5

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Observation 5bc6ba3c-2db5-4531-a909-7f282c9624c5 · outbound

This paper cites Resound: Towards action recognition without representation bias,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Resound: Towards action recognition without representation bias,

Reference 6

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Observation 91d9f03f-84fd-4755-b73e-5bba505c99f0 · outbound

This paper cites Can masking background and object reduce static bias for zero-shot action recognition?.

MoExDA: Domain Adaptation for Edge-based Action Recognition Can masking background and object reduce static bias for zero-shot action recognition?

Reference 7

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Observation f50ae490-9292-498c-a536-78b3c05e35d8 · outbound

This paper cites Hu- man action recognition without human,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Hu- man action recognition without human,

Reference 8

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Observation e2f3a2e7-28df-4d32-ab2b-107745def72d · outbound

This paper cites What do 15,000 object categories tell us about classifying and localizing actions?.

MoExDA: Domain Adaptation for Edge-based Action Recognition What do 15,000 object categories tell us about classifying and localizing actions?

Reference 9

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Observation fd0a407f-ac87-49a6-89c8-3d96615f42c7 · outbound

This paper cites Harnessing object and scene semantics for large-scale video under- standing,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Harnessing object and scene semantics for large-scale video under- standing,

Reference 10

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Observation c1bc70ee-3d66-4fe4-956c-11e34e5b2d87 · outbound

This paper cites Is appearance free action recognition possible?.

MoExDA: Domain Adaptation for Edge-based Action Recognition Is appearance free action recognition possible?

Reference 11

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Observation ebce80eb-a5d5-4143-a10b-ee9c511ae7af · outbound

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

MoExDA: Domain Adaptation for Edge-based Action Recognition An image is worth 16x16 words: Transformers for image recogni- tion at scale,

Reference 12

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Observation a6ed6415-24b2-4a7d-9bc6-0ed2fb84055a · outbound

This paper cites On feature normalization and data augmenta- tion,.

MoExDA: Domain Adaptation for Edge-based Action Recognition On feature normalization and data augmenta- tion,

Reference 13

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Observation fa57a4c0-7205-4dd5-b33f-e0cc8c8c141e · outbound

This paper cites Quo vadis, action recog- nition? a new model and the kinetics dataset,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Quo vadis, action recog- nition? a new model and the kinetics dataset,

Reference 14

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Observation cb31a206-4269-4746-ab37-affc37af4123 · outbound

This paper cites X3d: Expanding architectures for efficient video recognition,.

MoExDA: Domain Adaptation for Edge-based Action Recognition X3d: Expanding architectures for efficient video recognition,

Reference 15

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Observation 078aee24-ae39-42e7-aaa2-33418d904e8c · outbound

This paper cites Slow- fast networks for video recognition,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Slow- fast networks for video recognition,

Reference 16

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Observation 4973f7bd-5178-4c2b-8003-0a416a5cbdbe · outbound

This paper cites Recent advances in video action recognition with 3d convolutions,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Recent advances in video action recognition with 3d convolutions,

Reference 17

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Observation 4ce0e026-e91b-46c3-85bc-670d1aaa3576 · outbound

This paper cites Vivit: A video vision transformer,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Vivit: A video vision transformer,

Reference 18

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Observation 986fc359-d662-425f-a0c3-3f1e2cab8d32 · outbound

This paper cites Is space- time attention all you need for video understanding?.

MoExDA: Domain Adaptation for Edge-based Action Recognition Is space- time attention all you need for video understanding?

Reference 19

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Observation 4c0549a7-a2c5-4892-9b4f-1e226e32dedf · outbound

This paper cites Video transform- ers: A survey,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Video transform- ers: A survey,

Reference 20

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Observation a7545139-75c6-4851-8039-3a8e0f98449a · outbound

This paper cites Video swin transformer,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Video swin transformer,

Reference 21

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Observation b6978c82-2eca-441b-ae37-5a9a38795e8f · outbound

This paper cites VideoCLIP: Contrastive pre- training for zero-shot video-text understanding,.

MoExDA: Domain Adaptation for Edge-based Action Recognition VideoCLIP: Contrastive pre- training for zero-shot video-text understanding,

Reference 22

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Observation 332235fb-b1dd-4aa6-915b-f080b3dd2d37 · outbound

This paper cites ActionCLIP: A New Paradigm for Video Action Recognition.

MoExDA: Domain Adaptation for Edge-based Action Recognition ActionCLIP: A New Paradigm for Video Action Recognition

Reference 23

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Observation a5e85006-7b66-4425-8715-8b85d73a383b · outbound

This paper cites Fine-tuned clip models are efficient video learners,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Fine-tuned clip models are efficient video learners,

Reference 24

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Observation a874e0a0-2137-4e49-b445-ce80fdc9213e · outbound

This paper cites S3aug: Segmenta- tion, sampling, and shift for action recognition,.

MoExDA: Domain Adaptation for Edge-based Action Recognition S3aug: Segmenta- tion, sampling, and shift for action recognition,

Reference 25

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Observation 5662af83-45bc-44c4-b3f2-cd1d55cae884 · outbound

This paper cites Mitigating and evaluating static bias of action representations in the background and the foreground,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Mitigating and evaluating static bias of action representations in the background and the foreground,

Reference 26

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Observation beeee3c5-b455-4343-9526-ffe26836096c · outbound

This paper cites Removing the background by adding the background: Towards back- ground robust self-supervised video representation learn- ing,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Removing the background by adding the background: Towards back- ground robust self-supervised video representation learn- ing,

Reference 27

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Observation ae4515bc-f202-4660-8f86-26ebc5ed8e99 · outbound

This paper cites Why can’t I dance in the mall? learn- ing to mitigate scene bias in action recognition,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Why can’t I dance in the mall? learn- ing to mitigate scene bias in action recognition,

Reference 28

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Observation 1e6b30fd-35c4-40af-aab9-16efd0023108 · outbound

This paper cites Devias: Learn- ing disentangled video representations of action and scene,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Devias: Learn- ing disentangled video representations of action and scene,

Reference 29

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Observation 6352e26e-bd36-4057-a190-5623c858f07d · outbound

This paper cites Mitigating representation bias in action recognition: Algorithms and benchmarks,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Mitigating representation bias in action recognition: Algorithms and benchmarks,

Reference 30

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Observation ff68d115-2c5c-42ea-82f9-4f837632003f · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

MoExDA: Domain Adaptation for Edge-based Action Recognition UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 31

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Observation cf41a237-4abb-40e3-b851-4704fce692f5 · outbound

This paper cites Action recognition using edge trajectories and motion acceleration descriptor,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Action recognition using edge trajectories and motion acceleration descriptor,

Reference 32

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Observation 8fb07a53-a977-43d5-881b-54946e0b79e8 · outbound

This paper cites An edge-based ap- proach to motion detection,.

MoExDA: Domain Adaptation for Edge-based Action Recognition An edge-based ap- proach to motion detection,

Reference 33

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Observation 6fd3df64-3521-4249-897a-0b79dc9e15f2 · outbound

This paper cites A sketch-based approach for detecting com- mon human actions,.

MoExDA: Domain Adaptation for Edge-based Action Recognition A sketch-based approach for detecting com- mon human actions,

Reference 34

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Observation 306a78da-4158-4659-b0de-d9be4a75614b · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,

Reference 35

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Observation ebfda124-3416-48cf-911e-bb418d559390 · outbound

This paper cites Instance Normalization: The Missing Ingredient for Fast Stylization.

MoExDA: Domain Adaptation for Edge-based Action Recognition Instance Normalization: The Missing Ingredient for Fast Stylization

Reference 36

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Observation d77f24db-3a6e-4e0e-8ebf-931da4e90048 · outbound

This paper cites Group Normalization,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Group Normalization,

Reference 37

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Observation 3bc3c6cc-e014-4745-9bef-aa2b5e8e123a · outbound

This paper cites Arbitrary style transfer in real-time with adaptive instance normalization,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Arbitrary style transfer in real-time with adaptive instance normalization,

Reference 38

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This paper cites Available: https://link.springer.com/ chapter/10.1007/978-3-030-01261-8 1.

MoExDA: Domain Adaptation for Edge-based Action Recognition Available: https://link.springer.com/ chapter/10.1007/978-3-030-01261-8 1

Reference 39

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Observation ae3da862-5d96-4e57-beef-3a0825f338c2 · outbound

This paper cites mixup: Beyond empirical risk minimization,.

MoExDA: Domain Adaptation for Edge-based Action Recognition mixup: Beyond empirical risk minimization,

Reference 40

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Observation cf2cff99-5a66-45c9-a7af-c5850f7eac88 · outbound

This paper cites Po- sitional normalization,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Po- sitional normalization,

Reference 41

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Observation 1d24cf5c-854d-428d-aa33-901ec8f2f06a · outbound

This paper cites Objectmix: Data augmentation by copy-pasting objects in videos for action recognition,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Objectmix: Data augmentation by copy-pasting objects in videos for action recognition,

Reference 42

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Observation 5b54d143-4592-4780-a77f-33a62c654afc · outbound

This paper cites VideoMix: Rethinking Data Augmentation for Video Classification.

MoExDA: Domain Adaptation for Edge-based Action Recognition VideoMix: Rethinking Data Augmentation for Video Classification

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-06T04:53:57.450811Z

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Observation 4fb86b90-7d50-48b0-9936-f739a7f19176 · outbound

This paper cites The Kinetics Human Action Video Dataset.

MoExDA: Domain Adaptation for Edge-based Action Recognition The Kinetics Human Action Video Dataset

Reference 44

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Observation bbc71818-c435-4bda-a648-90906631d744 · outbound

This paper cites Imagenet: A large-scale hierarchical im- age database,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Imagenet: A large-scale hierarchical im- age database,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:53:57.654399Z

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Observation 23dc6b31-cfbb-4bdc-bea3-224684e4e6f9 · outbound

This paper cites Mimetics: Towards understanding human actions out of context,.

MoExDA: Domain Adaptation for Edge-based Action Recognition Mimetics: Towards understanding human actions out of context,

Reference 47

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Observation 9aeb3cca-cdf1-42c6-86a3-81e2466df198 · outbound

This paper cites Available: https://doi.org/10.1007/ s11263-022-01594-9.

MoExDA: Domain Adaptation for Edge-based Action Recognition Available: https://doi.org/10.1007/ s11263-022-01594-9

Reference 2022

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Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.