Paper Citation Record · LEDGER

Scaling 4D Representations

As of 23 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 13 inbound Pith citation observations for arXiv:2412.15212.

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

pith.paper-citation-record.v1
2412.15212 v2

Coverage vector

measured 83 of 83 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:36:44.784379Z

measured 96 of 96 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:58:05.471418Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T00:26:39.628379Z

Reference resolution

83 of 83 outbound references displayed

verified exact0
verified fuzzy68
unresolved15
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0b339532-bb5e-4af6-a620-76975105b77b · outbound

This paper cites Learning to see by moving.

Scaling 4D Representations Learning to see by moving

Reference 1

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Source-reported events for the cited work

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Observation cf470e0f-5277-4e63-8bb8-17d39113ebec · outbound

This paper cites Flamingo: a visual language model for few-shot learning.NeurIPS, 2022.

Scaling 4D Representations Flamingo: a visual language model for few-shot learning.NeurIPS, 2022

Reference 2

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Source-reported events for the cited work

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Observation 662649da-014c-44dc-9414-82ada87ce246 · outbound

This paper cites Self-supervised learning by cross-modal audio-video clustering.

Scaling 4D Representations Self-supervised learning by cross-modal audio-video clustering

Reference 3

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Observation c10bbcd5-9c71-4960-8f22-2645104fbdd0 · outbound

This paper cites Look, listen and learn.

Scaling 4D Representations Look, listen and learn

Reference 4

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Observation c658eb3e-2ea4-46b3-a254-1705fe88234c · outbound

This paper cites Objects that sound.

Scaling 4D Representations Objects that sound

Reference 5

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Observation 16179761-9a9e-4210-a727-73d0c9be023b · outbound

This paper cites Vivit: A video vi- sion transformer.

Scaling 4D Representations Vivit: A video vi- sion transformer

Reference 6

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Observation 7eddce4d-481e-42b1-8e33-4682642feebd · outbound

This paper cites Yuille, Trevor Darrell, Jitendra Malik, and Alexei A.

Scaling 4D Representations Yuille, Trevor Darrell, Jitendra Malik, and Alexei A

Reference 7

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Observation a0c4a0cc-5f7d-425e-a74c-82815ff07527 · outbound

This paper cites Revisiting feature prediction for learning visual rep- resentations from video.

Scaling 4D Representations Revisiting feature prediction for learning visual rep- resentations from video

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:46.308947Z

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Observation 2ca458b1-eaac-4e82-8075-37dd30dac47c · outbound

This paper cites Physion: Evaluating physical prediction from vision in humans and machines.

Scaling 4D Representations Physion: Evaluating physical prediction from vision in humans and machines

Reference 9

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Observation 952ee2be-95a3-42b3-a3b1-2f1536bec186 · outbound

This paper cites Zoedepth: Zero-shot transfer by com- bining relative and metric depth.

Scaling 4D Representations Zoedepth: Zero-shot transfer by com- bining relative and metric depth

Reference 10

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Observation 470119a5-a728-47d2-b01a-33f65d853cf0 · outbound

This paper cites Deep regression on manifolds: a 3d rotation case study.

Scaling 4D Representations Deep regression on manifolds: a 3d rotation case study

Reference 11

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Observation e7ab0d43-18e1-48cd-a0a1-431000193af7 · outbound

This paper cites Activitynet: A large-scale video benchmark for human activity understanding.

Scaling 4D Representations Activitynet: A large-scale video benchmark for human activity understanding

Reference 12

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Source-reported events for the cited work

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Observation e27a8fe5-b26e-4900-81b2-85849eaa52e1 · outbound

This paper cites Generative pre- training from pixels.

Scaling 4D Representations Generative pre- training from pixels

Reference 13

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Observation 78290ec3-c7e5-4978-a6ff-79e2078fb82e · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Scaling 4D Representations A simple framework for contrastive learning of visual representations

Reference 14

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Observation 1b2718de-c323-4f1a-954f-b0760812c3d6 · outbound

This paper cites PaLI-3 vision language models: Smaller, faster, stronger.

Scaling 4D Representations PaLI-3 vision language models: Smaller, faster, stronger

Reference 15

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Observation f7725220-fe7d-4be8-96c1-e8f4e56e71f3 · outbound

This paper cites A unified architecture for natural language processing: Deep neural networks with multitask learning.

Scaling 4D Representations A unified architecture for natural language processing: Deep neural networks with multitask learning

Reference 16

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Observation ebcf9b9b-d7fa-4559-a9a8-482ceb41f14d · outbound

This paper cites Scannet: Richly-annotated 3d reconstructions of indoor scenes.

Scaling 4D Representations Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 17

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Observation c7a9af97-80d3-422b-9df6-2facdef4b3c2 · outbound

This paper cites Scaling egocentric vision: The epic-kitchens dataset.

Scaling 4D Representations Scaling egocentric vision: The epic-kitchens dataset

Reference 18

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Observation 50e07444-1252-4e4e-9661-0d83d8d4a623 · outbound

This paper cites Scaling vision transformers to 22 billion pa- rameters.

Scaling 4D Representations Scaling vision transformers to 22 billion pa- rameters

Reference 19

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Source-reported events for the cited work

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Observation 4b68ec81-0a88-4fc0-8921-ee4363d57d59 · outbound

This paper cites Unsuper- vised visual representation learning by context prediction.

Scaling 4D Representations Unsuper- vised visual representation learning by context prediction

Reference 20

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Observation 2f9c645e-3faf-4d17-a87a-a946352dbe79 · outbound

This paper cites TAP-vid: A bench- mark for tracking any point in a video.

Scaling 4D Representations TAP-vid: A bench- mark for tracking any point in a video

Reference 21

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Observation 01d6782e-17ff-437c-840f-d051952c183f · outbound

This paper cites TAPIR: Tracking any point with per-frame initialization and temporal refinement.

Scaling 4D Representations TAPIR: Tracking any point with per-frame initialization and temporal refinement

Reference 22

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Observation 980b072f-a4a6-4b6e-85d1-10c0b558138a · outbound

This paper cites Boot- sTAP: Bootstrapped training for tracking any point.

Scaling 4D Representations Boot- sTAP: Bootstrapped training for tracking any point

Reference 23

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Source-reported events for the cited work

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Observation d85b95bb-494b-4c9b-a766-2e1751f1d87b · outbound

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

Scaling 4D Representations An image is worth 16x16 words: Transformers for image recognition at scale

Reference 24

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Observation 8149e4d6-62b2-46ba-90aa-2aee56ab9d2c · outbound

This paper cites Prob- ing the 3d awareness of visual foundation models.

Scaling 4D Representations Prob- ing the 3d awareness of visual foundation models

Reference 25

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Observation 11b57ec8-2b55-496c-81c8-ad1ab73851a7 · outbound

This paper cites Scalable pre- training of large autoregressive image models.

Scaling 4D Representations Scalable pre- training of large autoregressive image models

Reference 26

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Observation cbdcdae6-b054-4dca-82d1-2f5c2517b874 · outbound

This paper cites SA Vi++: Towards end-to-end object-centric learning from real-world videos.

Scaling 4D Representations SA Vi++: Towards end-to-end object-centric learning from real-world videos

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.927939Z

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Observation a6f3db81-a5af-404c-9abb-4bffa133347b · outbound

This paper cites Spatiotemporal residual networks for video action recogni- tion.

Scaling 4D Representations Spatiotemporal residual networks for video action recogni- tion

Reference 28

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This paper cites Slowfast networks for video recognition.

Scaling 4D Representations Slowfast networks for video recognition

Reference 29

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This paper cites A large-scale study on unsupervised spatiotemporal representation learning.

Scaling 4D Representations A large-scale study on unsupervised spatiotemporal representation learning

Reference 30

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Observation 4ac01222-34ab-43e4-96ec-48e8fc11e549 · outbound

This paper cites Distributed hier- archical processing in the primate cerebral cortex.

Scaling 4D Representations Distributed hier- archical processing in the primate cerebral cortex

Reference 31

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This paper cites Learning invariance from transformation se- quences.

Scaling 4D Representations Learning invariance from transformation se- quences

Reference 32

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Scaling 4D Representations Unresolved cited work

Reference 33

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This paper cites Learn- ing to linearize under uncertainty.

Scaling 4D Representations Learn- ing to linearize under uncertainty

Reference 34

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Observation 6a837ddb-613d-4c10-981b-8d4a1751f540 · outbound

This paper cites The” something something” video database for learning and evaluating visual common sense.

Scaling 4D Representations The” something something” video database for learning and evaluating visual common sense

Reference 35

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Scaling 4D Representations Unresolved cited work

Reference 36

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This paper cites Memory- augmented dense predictive coding for video representation learning.

Scaling 4D Representations Memory- augmented dense predictive coding for video representation learning

Reference 37

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This paper cites Self- supervised co-training for video representation learning.

Scaling 4D Representations Self- supervised co-training for video representation learning

Reference 38

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This paper cites Masked autoencoders are scalable vision learners.

Scaling 4D Representations Masked autoencoders are scalable vision learners

Reference 39

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Observation 43151a63-3d22-4585-bb0a-0e4fba8352f4 · outbound

This paper cites Data-efficient image recognition with contrastive predictive coding.

Scaling 4D Representations Data-efficient image recognition with contrastive predictive coding

Reference 40

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This paper cites Representation learn- ing with video deep infomax.

Scaling 4D Representations Representation learn- ing with video deep infomax

Reference 41

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This paper cites The kinetics human action video dataset, 2017.

Scaling 4D Representations The kinetics human action video dataset, 2017

Reference 42

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This paper cites Condi- tional object-centric learning from video.

Scaling 4D Representations Condi- tional object-centric learning from video

Reference 43

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Observation ffd50336-2e97-4534-9526-22ee3a190c0e · outbound

This paper cites Coopera- tive learning of audio and video models from self-supervised synchronization.

Scaling 4D Representations Coopera- tive learning of audio and video models from self-supervised synchronization

Reference 44

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Scaling 4D Representations Decoupled weight decay regularization

Reference 45

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This paper cites Object vision and spatial vision: two cortical path- ways.

Scaling 4D Representations Object vision and spatial vision: two cortical path- ways

Reference 46

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This paper cites Deep learning from temporal coherence in video.

Scaling 4D Representations Deep learning from temporal coherence in video

Reference 47

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This paper cites Audio- visual instance discrimination with cross-modal agreement.

Scaling 4D Representations Audio- visual instance discrimination with cross-modal agreement

Reference 48

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Observation b74c613d-5ea4-49af-b0d5-fb8aa63ba36c · outbound

This paper cites Atlas: End- to-end 3d scene reconstruction from posed images.

Scaling 4D Representations Atlas: End- to-end 3d scene reconstruction from posed images

Reference 49

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Scaling 4D Representations Unresolved cited work

Reference 50

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Observation c3d11847-36cb-4cf0-b3ba-cc83e4ae29a5 · outbound

This paper cites Fully sharded data parallel: faster ai training with fewer gpus.

Scaling 4D Representations Fully sharded data parallel: faster ai training with fewer gpus

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.494406Z

Source-reported events for the cited work

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Observation 6094bae1-83ff-4d21-91e8-ce3e0944b628 · outbound

This paper cites Audio-visual scene analysis with self-supervised multisensory features.

Scaling 4D Representations Audio-visual scene analysis with self-supervised multisensory features

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.461267Z

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Observation 73e05ce9-cdde-4a70-9aff-550ffe691b5a · outbound

This paper cites Context encoders: Feature learning by inpainting.

Scaling 4D Representations Context encoders: Feature learning by inpainting

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.432140Z

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Observation 92e1b67d-3531-45b7-af13-56a231503c45 · outbound

This paper cites Learning features by watching ob- jects move.

Scaling 4D Representations Learning features by watching ob- jects move

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.411765Z

Source-reported events for the cited work

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Observation 65957157-1fdd-4a39-a0fd-c759560f712c · outbound

This paper cites Perception test: A diagnostic benchmark for mul- timodal video models.

Scaling 4D Representations Perception test: A diagnostic benchmark for mul- timodal video models

Reference 55

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Observation e6b5101f-2a7a-464c-b2c9-8edb3d41da86 · outbound

This paper cites Asano, Ruth Fong, Jo ˜ao F.

Scaling 4D Representations Asano, Ruth Fong, Jo ˜ao F

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.365374Z

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Observation 96ae4680-7f6b-4366-9b2d-4559f66f0d26 · outbound

This paper cites Spatiotempo- ral contrastive video representation learning.

Scaling 4D Representations Spatiotempo- ral contrastive video representation learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.342771Z

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This paper cites Improving language understanding by generative pre-training.

Scaling 4D Representations Improving language understanding by generative pre-training

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.325299Z

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This paper cites Learning transferable visual models from natural language supervision, 2021.

Scaling 4D Representations Learning transferable visual models from natural language supervision, 2021

Reference 59

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Scaling 4D Representations Vi- sion transformers for dense prediction

Reference 60

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Scaling 4D Representations Video (lan- guage) modeling: a baseline for generative models of natural videos

Reference 61

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Observation 486ee18c-3a64-4bcc-89aa-9fb47a615e7f · outbound

This paper cites Broaden your views for self-supervised video learning.

Scaling 4D Representations Broaden your views for self-supervised video learning

Reference 62

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This paper cites Learning to localize sound source in visual scenes.

Scaling 4D Representations Learning to localize sound source in visual scenes

Reference 63

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This paper cites Two-stream con- volutional networks for action recognition in videos.

Scaling 4D Representations Two-stream con- volutional networks for action recognition in videos

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.224949Z

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Observation 8fe5f959-0955-4d4d-90ad-2085c89c9540 · outbound

This paper cites A Short Note on the Kinetics-700-2020 Human Action Dataset.

Scaling 4D Representations A Short Note on the Kinetics-700-2020 Human Action Dataset

Reference 65

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This paper cites Scalability in perception for autonomous driving: Waymo open dataset.

Scaling 4D Representations Scalability in perception for autonomous driving: Waymo open dataset

Reference 66

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Observation 2616d095-999d-4c51-a0ef-5ea8d62de70e · outbound

This paper cites Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training.

Scaling 4D Representations Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.186125Z

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Observation 39d41e55-08ef-43df-9ac7-34f1a761a08f · outbound

This paper cites RealEstate10K.

Scaling 4D Representations RealEstate10K

Reference 68

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This paper cites Hud- son, Thomas Albert Keck, Joao Carreira, Alexey Dosovit- skiy, Mehdi S.

Scaling 4D Representations Hud- son, Thomas Albert Keck, Joao Carreira, Alexey Dosovit- skiy, Mehdi S

Reference 69

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Observation 5d725e0d-58d4-492f-93fc-3c7d05034ef2 · outbound

This paper cites An- ticipating the future by watching unlabeled video.

Scaling 4D Representations An- ticipating the future by watching unlabeled video

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.127424Z

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Observation a828f910-15b3-46a5-9d5b-589b1b30b86c · outbound

This paper cites Videomae v2: Scaling video masked autoencoders with dual masking.

Scaling 4D Representations Videomae v2: Scaling video masked autoencoders with dual masking

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.108616Z

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Observation c361c815-92a8-4ac2-89f9-9204a67a5105 · outbound

This paper cites Masked video distillation: Rethinking masked feature mod- eling for self-supervised video representation learning.

Scaling 4D Representations Masked video distillation: Rethinking masked feature mod- eling for self-supervised video representation learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.082943Z

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This paper cites Dust3r: Geometric 3d vi- sion made easy.

Scaling 4D Representations Dust3r: Geometric 3d vi- sion made easy

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.058181Z

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Observation b4072c0e-7f1c-4142-9901-00ed740a4c4e · outbound

This paper cites Unsupervised learning of visual representations using videos.

Scaling 4D Representations Unsupervised learning of visual representations using videos

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T11:36:44.732519Z

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This paper cites Internvideo: General video foundation models via generative and discriminative learning.

Scaling 4D Representations Internvideo: General video foundation models via generative and discriminative learning

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.023854Z

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Observation 1e6ea0ac-238f-432b-8a31-1b7cad8c415e · outbound

This paper cites Less is more: Consistent video depth estimation with masked frames modeling.

Scaling 4D Representations Less is more: Consistent video depth estimation with masked frames modeling

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:45.005827Z

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This paper cites Controlling space and time with dif- fusion models.

Scaling 4D Representations Controlling space and time with dif- fusion models

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:44.987316Z

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Observation 75cdb228-4dec-4e78-9775-9b3c396b71e3 · outbound

This paper cites Slow feature analysis: Unsupervised learning of invariances.

Scaling 4D Representations Slow feature analysis: Unsupervised learning of invariances

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:44.963970Z

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Observation c514da1a-3ac7-45eb-9771-de72d24d97c7 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

Scaling 4D Representations Depth anything: Unleashing the power of large-scale unlabeled data

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:44.944943Z

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This paper cites Sigmoid loss for language image pre-training.

Scaling 4D Representations Sigmoid loss for language image pre-training

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:44.927363Z

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This paper cites A general protocol to probe large vision models for 3d physical understanding.

Scaling 4D Representations A general protocol to probe large vision models for 3d physical understanding

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:44.909064Z

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This paper cites Videoprism: A foundational visual encoder for video understanding.

Scaling 4D Representations Videoprism: A foundational visual encoder for video understanding

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:44.889195Z

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Observation f2a9b05a-c703-41e1-a744-00cf8fe2d95d · outbound

This paper cites Taking something from somewhere.

Scaling 4D Representations Taking something from somewhere

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:36:44.863759Z

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Pith citing papers

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From Image to Video: An Empirical Study of Diffusion Representations cites this paper.

From Image to Video: An Empirical Study of Diffusion Representations Scaling 4D Representations

Reference 11

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MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning cites this paper.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Scaling 4D Representations

Reference 11

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V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning cites this paper.

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning Scaling 4D Representations

Reference 12

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Observation cd2fcdce-93e6-443e-a1d2-e60e744413ca · inbound

SciVid: Cross-Domain Evaluation of Video Models in Scientific Applications cites this paper.

SciVid: Cross-Domain Evaluation of Video Models in Scientific Applications Scaling 4D Representations

Reference 11

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Observation 987e4f4f-0610-45b9-a150-53cfdb66061e · inbound

Frozen Forecasting: A Unified Evaluation cites this paper.

Frozen Forecasting: A Unified Evaluation Scaling 4D Representations

Reference 4

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Observation b38649bd-72f1-4353-8d6a-7c3454b70b61 · inbound

Unique Lives, Shared World: Learning from Single-Life Videos cites this paper.

Unique Lives, Shared World: Learning from Single-Life Videos Scaling 4D Representations

Reference 7

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LA-Pose: Latent Action Pretraining Meets Pose Estimation cites this paper.

LA-Pose: Latent Action Pretraining Meets Pose Estimation Scaling 4D Representations

Reference 8

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Observation 7f655eea-b5fc-4541-8a3f-5952a9598b38 · inbound

Towards Data-Efficient Video Pre-training with Frozen Image Foundation Models cites this paper.

Towards Data-Efficient Video Pre-training with Frozen Image Foundation Models Scaling 4D Representations

Reference 4

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Observation 394c74aa-a514-4f79-96b3-0293b0ebf52c · inbound

Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation? cites this paper.

Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation? Scaling 4D Representations

Reference 9

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Observation a3458577-a4a7-4f4c-a660-a63981166db6 · inbound

Gen4U: Unifying Video Generation and Understanding via Diffusion cites this paper.

Gen4U: Unifying Video Generation and Understanding via Diffusion Scaling 4D Representations

Reference 4

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Observation 50b5a864-d3ae-4dc9-a564-937d49a0c200 · inbound

SeeSE3: Emergence of 3D Space in Vision Features cites this paper.

SeeSE3: Emergence of 3D Space in Vision Features Scaling 4D Representations

Reference 70

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Observation 9b3a923d-c9c3-4986-9f24-86606157a485 · inbound

Self-Supervised Learning of Structured Dynamics from Videos cites this paper.

Self-Supervised Learning of Structured Dynamics from Videos Scaling 4D Representations

Reference 7

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Observation 623c607f-1b6e-4707-bb06-c685e28ce65f · inbound

A Controlled Study of Self-Supervised Image and Video Pretraining under Limited Resources cites this paper.

A Controlled Study of Self-Supervised Image and Video Pretraining under Limited Resources Scaling 4D Representations

Reference 9

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