Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:29:26.600144Z
Paper Citation Record · LEDGER
How to Design and Train Your Implicit Neural Representation for Video Compression
As of 7 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2506.24127.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
65 of 65 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation e4c26d2f-0267-4027-a87f-1988222b6a66 · outbound
This paper cites Scale-space flow for end-to-end optimized video com- pression.
How to Design and Train Your Implicit Neural Representation for Video Compression Scale-space flow for end-to-end optimized video com- pression
Reference 1
- Resolution
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Observation 524c8850-05a6-41ff-9201-244564db9a8c · outbound
This paper cites an unresolved cited work.
How to Design and Train Your Implicit Neural Representation for Video Compression Unresolved cited work
Reference 2
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Observation aeb48e44-709c-4c22-99ca-ad934a1c6a80 · outbound
This paper cites Nerv: Neural representations for videos.Advances in Neural Infor- mation Processing Systems, 34:21557–21568, 2021.
How to Design and Train Your Implicit Neural Representation for Video Compression Nerv: Neural representations for videos.Advances in Neural Infor- mation Processing Systems, 34:21557–21568, 2021
Reference 3
- Resolution
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Observation f72ba59f-887f-4036-86aa-5fe65c3ba703 · outbound
This paper cites Cnerv: Content-adaptive neural representation for visual data, 2022.
How to Design and Train Your Implicit Neural Representation for Video Compression Cnerv: Content-adaptive neural representation for visual data, 2022
Reference 4
- Resolution
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Observation 99736da1-19af-4f1c-b34d-e2c18593067b · outbound
This paper cites Hnerv: A hybrid neural representa- tion for videos.
How to Design and Train Your Implicit Neural Representation for Video Compression Hnerv: A hybrid neural representa- tion for videos
Reference 5
- Resolution
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Source-reported events for the cited work
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Observation 09c6ea5f-6f0e-420b-a247-543982ae920b · outbound
This paper cites Fast encoding and decoding for implicit video representation, 2024.
How to Design and Train Your Implicit Neural Representation for Video Compression Fast encoding and decoding for implicit video representation, 2024
Reference 6
- Resolution
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Source-reported events for the cited work
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Observation 3534d657-bc33-4bb5-a1f1-35da869310f5 · outbound
This paper cites Transformers as meta-learners for implicit neural representations,.
How to Design and Train Your Implicit Neural Representation for Video Compression Transformers as meta-learners for implicit neural representations,
Reference 7
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 177f6cab-b558-48b2-a3e8-dd9e7fda6382 · outbound
This paper cites COIN: COmpression with Implicit Neural representations.
How to Design and Train Your Implicit Neural Representation for Video Compression COIN: COmpression with Implicit Neural representations
Reference 8
- Resolution
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Source-reported events for the cited work
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Observation f7538b33-a1a5-4025-89f2-2dc2f1ecef61 · outbound
This paper cites COIN++: Neural Compression Across Modalities.
How to Design and Train Your Implicit Neural Representation for Video Compression COIN++: Neural Compression Across Modalities
Reference 9
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8781379c-fa96-45eb-8c5d-0df5f133f439 · outbound
This paper cites Shacira: Scalable hash-grid compression for implicit neural representations, 2023.
How to Design and Train Your Implicit Neural Representation for Video Compression Shacira: Scalable hash-grid compression for implicit neural representations, 2023
Reference 10
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation feac1c02-3a3e-4b85-91a6-09d8b20d8915 · outbound
This paper cites Adversarial text to continuous image gener- ation.
How to Design and Train Your Implicit Neural Representation for Video Compression Adversarial text to continuous image gener- ation
Reference 11
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation aa1abb29-67b9-4f54-b5ff-60351fe09669 · outbound
This paper cites Towards scalable neural repre- sentation for diverse videos.
How to Design and Train Your Implicit Neural Representation for Video Compression Towards scalable neural repre- sentation for diverse videos
Reference 12
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation edaec6b1-4e5d-4fda-9d66-b26ba474a99f · outbound
This paper cites Gaussian error lin- ear units (gelus), 2023.
How to Design and Train Your Implicit Neural Representation for Video Compression Gaussian error lin- ear units (gelus), 2023
Reference 13
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 446fb8de-a8a7-44d6-9037-a5089a1a791a · outbound
This paper cites The ki- netics human action video dataset, 2017.
How to Design and Train Your Implicit Neural Representation for Video Compression The ki- netics human action video dataset, 2017
Reference 14
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 6bc6147d-9e56-4b81-ab98-d63b1bef7a9a · outbound
This paper cites Efficient video compression via content-adaptive super-resolution.ICCV, 2021.
How to Design and Train Your Implicit Neural Representation for Video Compression Efficient video compression via content-adaptive super-resolution.ICCV, 2021
Reference 15
- Resolution
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Source-reported events for the cited work
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Observation 4b56f2fa-0f32-4098-9338-b7f2fa6d4dc6 · outbound
This paper cites Generalizable Implicit Neural Representations via Instance Pattern Composers.
How to Design and Train Your Implicit Neural Representation for Video Compression Generalizable Implicit Neural Representations via Instance Pattern Composers
Reference 16
- Resolution
- verified exact
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Source-reported events for the cited work
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Observation 8feb5534-414d-436f-ade3-61bb4f1b6618 · outbound
This paper cites C3: High-performance and low-complexity neural compression from a single image or video, 2023.
How to Design and Train Your Implicit Neural Representation for Video Compression C3: High-performance and low-complexity neural compression from a single image or video, 2023
Reference 17
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation dad725ca-bc23-4b29-8f1c-e6af446e1517 · outbound
This paper cites Springer Nature Switzerland, 2024.
How to Design and Train Your Implicit Neural Representation for Video Compression Springer Nature Switzerland, 2024
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation df94d4bb-85c8-4ee0-81e5-e6b89ee8c178 · outbound
This paper cites Scalable neural video representations with learnable positional features.
How to Design and Train Your Implicit Neural Representation for Video Compression Scalable neural video representations with learnable positional features
Reference 19
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 82815768-f49d-4808-9bb9-aab9c3cdd5ef · outbound
This paper cites Kingma and Jimmy Ba.
How to Design and Train Your Implicit Neural Representation for Video Compression Kingma and Jimmy Ba
Reference 20
- Resolution
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Source-reported events for the cited work
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Observation 4f09abe8-bf76-41f7-9f19-8c4977c65a30 · outbound
This paper cites Hinerv: Video compression with hi- erarchical encoding-based neural representation.
How to Design and Train Your Implicit Neural Representation for Video Compression Hinerv: Video compression with hi- erarchical encoding-based neural representation
Reference 21
- Resolution
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Source-reported events for the cited work
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Observation 351e4827-15bb-46cd-a78b-7fad8cb1a8e0 · outbound
This paper cites Nvrc: Neural video representation compression, 2024.
How to Design and Train Your Implicit Neural Representation for Video Compression Nvrc: Neural video representation compression, 2024
Reference 22
- Resolution
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Source-reported events for the cited work
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Observation f924c27b-e70b-4cf6-a1d3-a7c87f69a7fc · outbound
This paper cites Cool-chic: Coordinate- based low complexity hierarchical image codec, 2023.
How to Design and Train Your Implicit Neural Representation for Video Compression Cool-chic: Coordinate- based low complexity hierarchical image codec, 2023
Reference 23
- Resolution
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Source-reported events for the cited work
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Observation 7af997b6-c970-409b-8782-3f79fbd88a0e · outbound
This paper cites Mpeg: A video compression standard for multimedia applications.Commun.
How to Design and Train Your Implicit Neural Representation for Video Compression Mpeg: A video compression standard for multimedia applications.Commun
Reference 24
- Resolution
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Source-reported events for the cited work
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Observation 3f4b6780-1476-434f-8676-7e5ac7d117c7 · outbound
This paper cites Ffnerv: Flow-guided frame-wise neural representations for videos.
How to Design and Train Your Implicit Neural Representation for Video Compression Ffnerv: Flow-guided frame-wise neural representations for videos
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 6fdc11a1-665b-4220-b556-d163e4998724 · outbound
This paper cites Deep contextual video compression, 2021.
How to Design and Train Your Implicit Neural Representation for Video Compression Deep contextual video compression, 2021
Reference 26
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 818ef064-5992-4c3a-acaa-51a2ff6983e5 · outbound
This paper cites Hybrid spatial- temporal entropy modelling for neural video compres- sion.
How to Design and Train Your Implicit Neural Representation for Video Compression Hybrid spatial- temporal entropy modelling for neural video compres- sion
Reference 27
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation d7d01c5c-e2b4-4f1a-b850-90e7d53b3c96 · outbound
This paper cites Neural video com- pression with diverse contexts.
How to Design and Train Your Implicit Neural Representation for Video Compression Neural video com- pression with diverse contexts
Reference 28
- Resolution
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Source-reported events for the cited work
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Observation 3231a152-e8b1-49c4-9346-0cd7c676bbdd · outbound
This paper cites Neural video compres- sion with feature modulation.
How to Design and Train Your Implicit Neural Representation for Video Compression Neural video compres- sion with feature modulation
Reference 29
- Resolution
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Source-reported events for the cited work
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Observation d146a97f-9b9b-4ee4-9dbb-2e53b1369444 · outbound
This paper cites E-nerv: Expedite neural video representation with disentangled spatial- temporal context, 2022.
How to Design and Train Your Implicit Neural Representation for Video Compression E-nerv: Expedite neural video representation with disentangled spatial- temporal context, 2022
Reference 30
- Resolution
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Observation ed39ff85-82b1-430e-8652-90720d4357f8 · outbound
This paper cites Neural Video Compression using Spatio-Temporal Priors.
How to Design and Train Your Implicit Neural Representation for Video Compression Neural Video Compression using Spatio-Temporal Priors
Reference 31
- Resolution
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Observation bd93148e-c69d-43eb-8aba-7bca649d3629 · outbound
This paper cites Nirvana: Neural implicit representations of videos with adaptive networks and autoregressive patch-wise modeling.
How to Design and Train Your Implicit Neural Representation for Video Compression Nirvana: Neural implicit representations of videos with adaptive networks and autoregressive patch-wise modeling
Reference 32
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 421f6669-170b-4bfa-b037-bf865e3efa29 · outbound
This paper cites Latent-inr: A flexible framework for implicit repre- sentations of videos with discriminative semantics.
How to Design and Train Your Implicit Neural Representation for Video Compression Latent-inr: A flexible framework for implicit repre- sentations of videos with discriminative semantics
Reference 33
- Resolution
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Source-reported events for the cited work
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Observation db264480-8b30-425e-b3b1-fcebc5026fcf · outbound
This paper cites Practical full resolution learned lossless image compression.
How to Design and Train Your Implicit Neural Representation for Video Compression Practical full resolution learned lossless image compression
Reference 34
- Resolution
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Observation d858c2eb-baea-4483-8619-3cc1c9069f55 · outbound
This paper cites Uvg dataset: 50/120fps 4k sequences for video codec analysis and development.
How to Design and Train Your Implicit Neural Representation for Video Compression Uvg dataset: 50/120fps 4k sequences for video codec analysis and development
Reference 35
- Resolution
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Source-reported events for the cited work
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Observation 95c3da05-6830-4dea-a595-ebbb8a284584 · outbound
This paper cites Srinivasan, Matthew Tancik, Jonathan T.
How to Design and Train Your Implicit Neural Representation for Video Compression Srinivasan, Matthew Tancik, Jonathan T
Reference 36
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2b69f69a-2f5d-4988-8c14-5e220e3e14d9 · outbound
This paper cites Instant neural graphics primitives with a multiresolution hash encoding.ACM Transac- tions on Graphics, 41(4):1–15, 2022.
How to Design and Train Your Implicit Neural Representation for Video Compression Instant neural graphics primitives with a multiresolution hash encoding.ACM Transac- tions on Graphics, 41(4):1–15, 2022
Reference 37
- Resolution
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Source-reported events for the cited work
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Observation 7dab789c-16cb-4bd5-9a04-3a75a5bbcf81 · outbound
This paper cites Explaining the implicit neural canvas: Connecting pixels to neurons by tracing their contri- butions.
How to Design and Train Your Implicit Neural Representation for Video Compression Explaining the implicit neural canvas: Connecting pixels to neurons by tracing their contri- butions
Reference 38
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation d09aae84-407e-4b66-9b25-b2f830ab979a · outbound
This paper cites Anderson, and Lubomir Bourdev.
How to Design and Train Your Implicit Neural Representation for Video Compression Anderson, and Lubomir Bourdev
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:34.690608Z
Source-reported events for the cited work
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Observation aa485d69-0a05-4e84-87e1-cb4afe68ef97 · outbound
This paper cites Anderson, Kedar Tat- wawadi, Sanjay Nair, Craig Lytle, and Lubomir Bour- dev.
How to Design and Train Your Implicit Neural Representation for Video Compression Anderson, Kedar Tat- wawadi, Sanjay Nair, Craig Lytle, and Lubomir Bour- dev
Reference 40
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 01ae1b5d-2a09-4e56-891c-af974f75bb92 · outbound
This paper cites Combining frame and gop embeddings for neural video representation.
How to Design and Train Your Implicit Neural Representation for Video Compression Combining frame and gop embeddings for neural video representation
Reference 41
- Resolution
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- raw_fallback, observed 2026-08-06T21:29:34.054483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5634a17b-1b9f-433b-a34a-0bce2202f2da · outbound
This paper cites Baraniuk.
How to Design and Train Your Implicit Neural Representation for Video Compression Baraniuk
Reference 42
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ca6ac8a5-5b2b-4025-8fbf-4dc241018cc4 · outbound
This paper cites Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang.
How to Design and Train Your Implicit Neural Representation for Video Compression Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:33.527310Z
Source-reported events for the cited work
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Observation d8346365-0cda-4af6-825b-ff713366f8a1 · outbound
This paper cites Implicit neural representations with periodic activation functions.Ad- vances in neural information processing systems, 33: 7462–7473, 2020.
How to Design and Train Your Implicit Neural Representation for Video Compression Implicit neural representations with periodic activation functions.Ad- vances in neural information processing systems, 33: 7462–7473, 2020
Reference 44
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 998f3ba3-3ced-4b75-8946-18fb814444c0 · outbound
This paper cites Adversarial generation of continuous images,.
How to Design and Train Your Implicit Neural Representation for Video Compression Adversarial generation of continuous images,
Reference 45
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 45e7bbe1-89f6-4bbf-b27a-2855b477b8bd · outbound
This paper cites Ucf101: A dataset of 101 human actions classes from videos in the wild, 2012.
How to Design and Train Your Implicit Neural Representation for Video Compression Ucf101: A dataset of 101 human actions classes from videos in the wild, 2012
Reference 46
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9e72c148-2c8f-478d-bbf2-74aae80ffabe · outbound
This paper cites Implicit neural represen- tations for image compression, 2022.
How to Design and Train Your Implicit Neural Representation for Video Compression Implicit neural represen- tations for image compression, 2022
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:32.723063Z
Source-reported events for the cited work
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Observation 559457bd-84b3-48dd-a089-fb3bb824d7e8 · outbound
This paper cites Sullivan, Jens-Rainer Ohm, Woo-Jin Han, and Thomas Wiegand.
How to Design and Train Your Implicit Neural Representation for Video Compression Sullivan, Jens-Rainer Ohm, Woo-Jin Han, and Thomas Wiegand
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:32.522999Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 03de008f-31e6-4bed-be4c-4049b7b4bb9a · outbound
This paper cites Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Sing- hal, Ravi Ramamoorthi, Jonathan T.
How to Design and Train Your Implicit Neural Representation for Video Compression Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Sing- hal, Ravi Ramamoorthi, Jonathan T
Reference 49
- Resolution
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- raw_fallback, observed 2026-08-06T21:29:32.220733Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8875643e-8bb5-41d6-910c-6912207360a3 · outbound
This paper cites Multiscale structural similarity for image quality as- sessment.
How to Design and Train Your Implicit Neural Representation for Video Compression Multiscale structural similarity for image quality as- sessment
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:32.013038Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 70e7105b-15cf-4d6d-8e12-593df52ca966 · outbound
This paper cites Wiegand, G.J.
How to Design and Train Your Implicit Neural Representation for Video Compression Wiegand, G.J
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:31.766265Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e3a3b5b7-25b6-4645-91dc-7987d2952f07 · outbound
This paper cites Qs-nerv: Real-time quality-scalable de- coding with neural representation for videos.
How to Design and Train Your Implicit Neural Representation for Video Compression Qs-nerv: Real-time quality-scalable de- coding with neural representation for videos
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:31.551105Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 26c35f92-2cf1-429a-bad5-2ad5a19970ed · outbound
This paper cites Signal processing for implicit neu- ral representations, 2022.
How to Design and Train Your Implicit Neural Representation for Video Compression Signal processing for implicit neu- ral representations, 2022
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:31.324082Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9e055f16-5ad8-4ad2-a660-ff912e8e3283 · outbound
This paper cites Vq-nerv: A vector quantized neural representation for videos, 2024.
How to Design and Train Your Implicit Neural Representation for Video Compression Vq-nerv: A vector quantized neural representation for videos, 2024
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:31.068279Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f80c8dcb-2270-48dc-9c7c-414d66f63356 · outbound
This paper cites Ds-nerv: Implicit neural video representation with decomposed static and dynamic codes.
How to Design and Train Your Implicit Neural Representation for Video Compression Ds-nerv: Implicit neural video representation with decomposed static and dynamic codes
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:30.863026Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation fb8befe3-48e1-46a9-a5cc-eb425c74a08f · outbound
This paper cites Gener- ating videos with dynamics-aware implicit generative adversarial networks, 2022.
How to Design and Train Your Implicit Neural Representation for Video Compression Gener- ating videos with dynamics-aware implicit generative adversarial networks, 2022
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:30.639028Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation fb3092a3-b506-4547-ba3e-b977a691dfb8 · outbound
This paper cites Boosting neural representations for videos with a con- ditional decoder.
How to Design and Train Your Implicit Neural Representation for Video Compression Boosting neural representations for videos with a con- ditional decoder
Reference 57
- Resolution
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- raw_fallback, observed 2026-08-06T21:29:30.478260Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4820c7f6-33e1-4200-8605-e16b5a17ebbd · outbound
This paper cites Implicit neural video compression, 2021.
How to Design and Train Your Implicit Neural Representation for Video Compression Implicit neural video compression, 2021
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:29.719763Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5a83b70e-4a9d-4899-9bd9-4164b82c360e · outbound
This paper cites Salman Asif, and Zhan Ma.
How to Design and Train Your Implicit Neural Representation for Video Compression Salman Asif, and Zhan Ma
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:28.834182Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ecff8652-5771-42d8-a78f-1d9866b3848e · outbound
This paper cites Salman Asif, and Zhan Ma.
How to Design and Train Your Implicit Neural Representation for Video Compression Salman Asif, and Zhan Ma
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:28.629195Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1c819b93-4d79-4520-97db-9c218a4913b5 · outbound
This paper cites short” train- ing time in Figure 13, and for “medium.
How to Design and Train Your Implicit Neural Representation for Video Compression short” train- ing time in Figure 13, and for “medium
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:28.364805Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 36e3bd74-821e-4846-9476-37b888396039 · outbound
This paper cites short” training time. Figure 28.Quality vs. size, for Honey- Bee (top) and Jockey (bottom) at 1080p with “medium.
How to Design and Train Your Implicit Neural Representation for Video Compression short” training time. Figure 28.Quality vs. size, for Honey- Bee (top) and Jockey (bottom) at 1080p with “medium
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:28.095086Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0a6314e2-5cdd-4acb-a9cd-e3385da01b9f · outbound
This paper cites See a detailed diagram of the first NeRV block corresponding to that stem in Figure 35.
How to Design and Train Your Implicit Neural Representation for Video Compression See a detailed diagram of the first NeRV block corresponding to that stem in Figure 35
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:27.823344Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation cebacd6b-4883-412e-93ba-9cbb5277d1a6 · outbound
This paper cites unique parameters.
How to Design and Train Your Implicit Neural Representation for Video Compression unique parameters
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:29:27.646834Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 10f14227-7e17-416c-a178-168699e9758c · outbound
This paper cites Adapting XINC XINC, introduced in [38], is a framework designed to inves- tigate how neurons in an image or video INR encode signals they are trained to represent.
How to Design and Train Your Implicit Neural Representation for Video Compression Adapting XINC XINC, introduced in [38], is a framework designed to inves- tigate how neurons in an image or video INR encode signals they are trained to represent
Reference 65
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-06T21:29:27.349280Z
Source-reported events for the cited work
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Pith citing papers
No inbound Pith citation observations are available.