Paper Citation Record · LEDGER

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos

As of 20 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 3 inbound Pith citation observations for arXiv:2501.12254.

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

pith.paper-citation-record.v1
2501.12254 v3

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:24:35.479086Z

measured 38 of 38 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 2026-08-03T00:18:17.120207Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:46:46.286883Z

Reference resolution

35 of 35 outbound references displayed

verified exact4
verified fuzzy14
unresolved16
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d5e227af-af8e-475e-95dd-c45a07e479d6 · outbound

This paper cites Figure 7:Visualization of label merging by Memory Storyboard on SAYCam.Each image represents a temporal segment; segments sharing the same color bar have been merged.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Figure 7:Visualization of label merging by Memory Storyboard on SAYCam.Each image represents a temporal segment; segments sharing the same color bar have been merged

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:35.720148Z

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.

Observation 2ac06131-a723-4a1e-b80d-ca153636bf5f · outbound

This paper cites an unresolved cited work.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:24:35.770087Z

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.

Observation c25ea12a-640a-454b-aa3a-0241d6e74d4d · outbound

This paper cites Lassiter and David Slaw.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Lassiter and David Slaw

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:36.052706Z

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.

Observation 1c11a477-7eab-47e5-ae97-4d35897a5eb9 · outbound

This paper cites The results here demonstrate that applying the temporal contrastive loss only on data from long-term memory or on the entire training batch achieves the best performance.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos The results here demonstrate that applying the temporal contrastive loss only on data from long-term memory or on the entire training batch achieves the best performance

Reference 8

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T17:24:35.805265Z

Source-reported events for the cited work

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Observation 38485316-bc2f-4718-95e2-6dfdf6dca8d5 · outbound

This paper cites In particular, we note that Memory Storyboard significantly outperforms SimCLR when we sample more data fromM short (towards the right side of thex-axis).

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos In particular, we note that Memory Storyboard significantly outperforms SimCLR when we sample more data fromM short (towards the right side of thex-axis)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:35.736974Z

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.

Observation dfc7b646-49fa-46c9-a955-fe6980858239 · outbound

This paper cites Category-specific video summarization.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Category-specific video summarization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:36.035813Z

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.

Observation 34f19c00-3c78-4712-b822-aa11c031ab1f · outbound

This paper cites an unresolved cited work.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:24:35.787041Z

Source-reported events for the cited work

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Observation ec6fcb40-609e-4659-a137-9fb9a4a440be · outbound

This paper cites Megan M Saylor, Dare A Baldwin, Jodie A Baird, and Jennifer LaBounty.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Megan M Saylor, Dare A Baldwin, Jodie A Baird, and Jennifer LaBounty

Reference 13

Resolution: verified exact; doi, observed 2026-08-10T17:24:35.519723Z

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.

Observation d1250c1d-e873-409b-9d00-c98b637f688e · outbound

This paper cites Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:35.373947Z

Source-reported events for the cited work

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Observation 105e15f8-2b90-4c0a-9275-04afebcd8179 · outbound

This paper cites Neural event segmentation of continuous experience in human infants.Proceedings of the National Academy of Sciences, 119(43):e2200257119,.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Neural event segmentation of continuous experience in human infants.Proceedings of the National Academy of Sciences, 119(43):e2200257119,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:35.983774Z

Source-reported events for the cited work

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Observation 2ded037b-823a-4d5e-b655-eb89c445552d · outbound

This paper cites Large Batch Training of Convolutional Networks.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Large Batch Training of Convolutional Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:35.390270Z

Source-reported events for the cited work

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Observation 4ce73f80-193c-4f69-8b93-110d4223abc7 · outbound

This paper cites Video summarization with long short-term memory.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Video summarization with long short-term memory

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:35.966872Z

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.

Observation 27304f67-a02f-424a-8854-951e605ae76a · outbound

This paper cites Integrating Present and Past in Unsupervised Continual Learning.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Integrating Present and Past in Unsupervised Continual Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:35.400783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a32d723-3f48-4f4b-9eb9-681fe787c4ad · outbound

This paper cites In Memory Storyboard, we create two separate projectors for LT CLandL SSL.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos In Memory Storyboard, we create two separate projectors for LT CLandL SSL

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:35.950224Z

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.

Observation 5fcd11a4-e64a-4ac1-b9ee-7a1b128c652b · outbound

This paper cites For the SimSiam (Chen & He,.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos For the SimSiam (Chen & He,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:35.917456Z

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.

Observation 038a9f7a-aa15-47d3-9f87-d9db7d6bb566 · outbound

This paper cites For OAK evaluations, we use Faster R-CNN (Ren et al., 2015), a popular two-stage object detector.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos For OAK evaluations, we use Faster R-CNN (Ren et al., 2015), a popular two-stage object detector

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:35.869640Z

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.

Observation 4e2a057a-127a-43bc-a58d-13a03f046e70 · outbound

This paper cites an unresolved cited work.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:24:35.853082Z

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.

Observation ee49dce4-04b7-46d1-8da6-bf5b35aeb403 · outbound

This paper cites an unresolved cited work.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:24:35.837477Z

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.

Observation 05356a81-1edf-4e0b-8a73-d560809c6715 · outbound

This paper cites an unresolved cited work.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:24:35.822036Z

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.

Observation 4a65e5d4-34e2-4611-899c-ed93def29d7b · outbound

This paper cites an unresolved cited work.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:24:35.753514Z

Source-reported events for the cited work

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Observation a1e59e62-0c12-4b11-9369-0c91a9795d9c · outbound

This paper cites an unresolved cited work.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Unresolved cited work

Reference 112

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:24:35.933783Z

Source-reported events for the cited work

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Observation 4984fa47-c0c1-412f-96c4-5696af46e19d · outbound

This paper cites Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu, and Sung Ju Hwang.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu, and Sung Ju Hwang

Reference 1991

Resolution: verified exact; doi, observed 2026-08-10T17:24:35.536982Z

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

Observation 65d0fb8f-4366-46d3-a373-ed8ed47c30ca · outbound

This paper cites Class Incremental Online Streaming Learning.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Class Incremental Online Streaming Learning

Reference 2001

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:35.298212Z

Source-reported events for the cited work

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Observation c2114665-4381-424c-a7a2-8a79f37f9f92 · outbound

This paper cites Enrico Fini, Victor G Turrisi Da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, and Julien Mairal.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Enrico Fini, Victor G Turrisi Da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, and Julien Mairal

Reference 2011

Resolution: verified exact; doi, observed 2026-08-10T17:24:35.553627Z

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.

Observation 0bb7942b-b663-4a7c-b636-120fc3b8cbd0 · outbound

This paper cites Youssef Ezzyat and Lila Davachi.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Youssef Ezzyat and Lila Davachi

Reference 2013

Resolution: verified exact; doi, observed 2026-08-10T17:24:35.569420Z

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.

Observation 4d2dceea-a908-4e8c-9866-e5849e61d598 · outbound

This paper cites The influence of context boundaries on memory for the sequential order of events.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos The influence of context boundaries on memory for the sequential order of events

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:36.079222Z

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.

Observation e7ccd9c6-9fb3-4b3b-a08f-2ca0598178b1 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Representation Learning with Contrastive Predictive Coding

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:35.341442Z

Source-reported events for the cited work

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Observation 5037215d-2da9-4287-a23c-3232d354d4ba · outbound

This paper cites For iNaturalist-2018, we used the LARS (You et al.,.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos For iNaturalist-2018, we used the LARS (You et al.,

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:35.885010Z

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.

Observation 761d92d9-c6ca-4f1e-b62f-0a072f08249d · outbound

This paper cites Measuring event segmentation: An investigation into the stability of event bound- ary agreement across groups.Behavior Research Methods, 55, 04.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Measuring event segmentation: An investigation into the stability of event bound- ary agreement across groups.Behavior Research Methods, 55, 04

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:36.018149Z

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.

Observation d46ad19a-2630-4f2a-849c-653d96ea2f68 · outbound

This paper cites Online Unsupervised Learning of Visual Representations and Categories.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Online Unsupervised Learning of Visual Representations and Categories

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:35.352208Z

Source-reported events for the cited work

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Observation 220f8bf4-81c7-4b68-b975-269a05e076d9 · outbound

This paper cites Kingma and Jimmy Ba.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Kingma and Jimmy Ba

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:35.326162Z

Source-reported events for the cited work

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Observation 8f3692b0-ae02-4004-bb55-d423f276cbbb · outbound

This paper cites Contrastive multiview coding.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Contrastive multiview coding

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:36.000570Z

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.

Observation 7076c644-a8d3-4665-95d8-68e430ff88ab · outbound

This paper cites Predicting the Susceptibility of Examples to Catastrophic Forgetting.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos Predicting the Susceptibility of Examples to Catastrophic Forgetting

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:35.320674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd156a0e-b125-46bd-aca9-ffbefd1fc500 · outbound

This paper cites PooDLe: Pooled and dense self-supervised learning from naturalistic videos.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos PooDLe: Pooled and dense self-supervised learning from naturalistic videos

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:35.379411Z

Source-reported events for the cited work

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Observation efb32579-194f-4fe2-8731-0093cf3aee1d · outbound

This paper cites That is, we store 20 model checkpoints throughout the streaming training and evaluate them on mini-ImageNet and Labeled-S with SVM readout.

Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos That is, we store 20 model checkpoints throughout the streaming training and evaluate them on mini-ImageNet and Labeled-S with SVM readout

Reference 2048

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:35.901333Z

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.

Pith citing papers

Observation f09c8193-3c98-4aa4-8b08-51653a1005c0 · inbound

EgoEverything: A Benchmark for Human Behavior Inspired Long Context Egocentric Video Understanding in AR Environment cites this paper.

EgoEverything: A Benchmark for Human Behavior Inspired Long Context Egocentric Video Understanding in AR Environment Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:36:00.375286Z

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.

Observation 866196f9-ecc5-4699-9009-50344a874d33 · inbound

EgoEverything: A Benchmark for Human Behavior Inspired Long Context Egocentric Video Understanding in AR Environment cites this paper.

Reference 2021

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d759fd0c-20ab-483c-a78d-96a1c9263077 · inbound

Continual Visual and Verbal Learning Through a Child's Egocentric Input cites this paper.

Continual Visual and Verbal Learning Through a Child's Egocentric Input Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos

Reference 93

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T07:46:46.288215Z

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.