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Paper Citation Record · LEDGER

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation

As of 9 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2507.02271.

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

pith.paper-citation-record.v1
2507.02271 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:37:19.640172Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 69a048b8-bf54-4b91-a693-0876059b899b · outbound

This paper cites The Foley grail: The art of performing sound for film, games, and animation.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation The Foley grail: The art of performing sound for film, games, and animation

Reference 1

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Observation 1b4e5e8d-32a5-4203-9e59-b2e0858f143d · outbound

This paper cites Deep residual learning for image recog- nition.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Deep residual learning for image recog- nition

Reference 7

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Observation b0c46848-49bc-4f87-9f79-5d9cb000a8a2 · outbound

This paper cites Denoising diffusion probabilistic models.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Denoising diffusion probabilistic models

Reference 10

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Observation ace39fec-2829-4717-88cf-34db85b6c68e · outbound

This paper cites Densely connected convolutional networks.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Densely connected convolutional networks

Reference 11

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Observation 4f00a0c2-f96d-4e06-944f-345201604a8e · outbound

This paper cites Read, Watch and Scream! Sound Generation from Text and Video.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Read, Watch and Scream! Sound Generation from Text and Video

Reference 13

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Observation db35df16-0f1b-47b4-8715-a7105ef1ecd5 · outbound

This paper cites Diverse part dis- covery: Occluded person re-identification with part-aware transformer.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Diverse part dis- covery: Occluded person re-identification with part-aware transformer

Reference 15

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Observation ea159ff9-6ce8-4424-849c-83af5bdab436 · outbound

This paper cites Mitigating and evaluating static bias of ac- tion representations in the background and the foreground.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Mitigating and evaluating static bias of ac- tion representations in the background and the foreground

Reference 16

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Observation 6dce6e9e-f5dc-432a-b213-05cb950ed00d · outbound

This paper cites AudioLDM: Text-to-audio generation with latent diffusion models.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation AudioLDM: Text-to-audio generation with latent diffusion models

Reference 17

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Observation d62a57df-c626-45fd-b39a-a55d007b256a · outbound

This paper cites DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

Reference 18

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Observation 8f82e688-1ece-4d83-a2b4-dab92dded7ad · outbound

This paper cites Diff-foley: Synchronized video-to-audio synthesis with latent diffusion models.Advances in Neural Information Processing Systems, 36,.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Diff-foley: Synchronized video-to-audio synthesis with latent diffusion models.Advances in Neural Information Processing Systems, 36,

Reference 19

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Observation 5212535c-5019-435a-9b49-e5dc42b12f9c · outbound

This paper cites The filmmaker’s eye: The language of the lens: The power of lenses and the expressive cinematic image.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation The filmmaker’s eye: The language of the lens: The power of lenses and the expressive cinematic image

Reference 20

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Observation 25fe33b3-7429-46af-b274-99c798f89081 · outbound

This paper cites Masked generative video-to-audio transformers with enhanced synchronicity.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Masked generative video-to-audio transformers with enhanced synchronicity

Reference 22

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Observation e12fde5f-5029-4e60-9cde-02ca65adfc27 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation High-resolution image synthesis with latent diffusion models

Reference 24

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Observation e6ed0c00-47cb-4112-a669-9ad94db08c51 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 25

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Observation dbcb4214-e9cb-4123-a6c0-3656e012e064 · outbound

This paper cites Denoising diffusion implicit models.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Denoising diffusion implicit models

Reference 26

Resolution
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Source-reported events for the cited work

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Observation a879510a-756e-404b-aee7-f14b9d686219 · outbound

This paper cites Temporally Aligned Audio for Video with Autoregression.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Temporally Aligned Audio for Video with Autoregression

Reference 27

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Observation 5364271f-4cb6-4cee-88c7-b28e96dcf8dd · outbound

This paper cites Removing the background by adding the background: Towards background robust self- supervised video representation learning.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Removing the background by adding the background: Towards background robust self- supervised video representation learning

Reference 28

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Observation 61ca4cac-0cdc-4c81-b490-83829effb8c5 · outbound

This paper cites Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Frieren: Efficient Video-to-Audio Generation Network with Rectified Flow Matching

Reference 29

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Observation dcf0d7b2-3fe4-4f7c-8ca5-6778b45c9083 · outbound

This paper cites Sonicvisionlm: Playing sound with vi- sion language models.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Sonicvisionlm: Playing sound with vi- sion language models

Reference 30

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Observation c58c3f2a-5a27-4d6e-a756-6457b59f5e04 · outbound

This paper cites Data- distortion guided self-distillation for deep neural networks.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Data- distortion guided self-distillation for deep neural networks

Reference 31

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Observation 8a4a7f3c-41ba-4ec9-ba7c-be28db54ae05 · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Cutmix: Regularization strategy to train strong classifiers with localizable features

Reference 32

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Observation 13c81013-ec80-41f2-98a5-f4b191318ce7 · outbound

This paper cites Self- supervised scene de-occlusion.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Self- supervised scene de-occlusion

Reference 33

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Observation 7a4233b5-94f2-40d4-acc6-21b953d901bb · outbound

This paper cites Be your own teacher: Improve the performance of convolutional neural networks via self distillation.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Be your own teacher: Improve the performance of convolutional neural networks via self distillation

Reference 34

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Observation 1c65f088-3fe7-4bc2-9256-114872a13097 · outbound

This paper cites FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds

Reference 35

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Observation 990972e0-a7ab-4afc-afc2-e81b7f28873e · outbound

This paper cites Human orientation estimation un- der partial observation.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Human orientation estimation un- der partial observation

Reference 36

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Observation 085339fe-e831-4b9d-8a2b-553feb5cd84a · outbound

This paper cites Knowledge distillation by on-the-fly native ensemble.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Knowledge distillation by on-the-fly native ensemble

Reference 37

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Observation 4e6e21e1-ceb6-42c3-861e-f6b90451c68a · outbound

This paper cites Vggsound: A large-scale audio-visual dataset.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Vggsound: A large-scale audio-visual dataset

Reference 2014

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Observation ec3772c3-aca5-4cc1-9ee4-a98870fc188b · outbound

This paper cites Classifier-Free Diffusion Guidance.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Classifier-Free Diffusion Guidance

Reference 2015

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Observation 60093ecc-6da4-4ccd-9ebf-89a2145e40ac · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Distilling the Knowledge in a Neural Network

Reference 2016

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Observation e812d4d2-3a71-4312-be93-55647037a939 · outbound

This paper cites Taming visually guided sound generation.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Taming visually guided sound generation

Reference 2017

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Source-reported events for the cited work

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Observation 1d3a90e3-5301-4b14-9610-8063bbac5269 · outbound

This paper cites Pose-guided feature alignment for occluded person re-identification.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Pose-guided feature alignment for occluded person re-identification

Reference 2019

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Observation 73dbc52d-bd2e-4109-872d-1649253d7afb · outbound

This paper cites Diffusion models beat gans on image synthe- sis.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Diffusion models beat gans on image synthe- sis

Reference 2020

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Source-reported events for the cited work

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Observation 943e0a8e-ed34-4e15-b066-937d0624c7e3 · outbound

This paper cites Motion-aware contrastive video rep- resentation learning via foreground-background merging.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Motion-aware contrastive video rep- resentation learning via foreground-background merging

Reference 2021

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Source-reported events for the cited work

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Observation 58f6c621-ff98-454b-abce-0de6cd318e58 · outbound

This paper cites Conditional gener- ation of audio from video via foley analogies.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Conditional gener- ation of audio from video via foley analogies

Reference 2022

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raw_fallback, observed 2026-08-06T20:37:23.778413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:37:16.459442Z digest=sha256:18a6b9b7cb75c20c6962ab462d4393928082fe9260737a830b289269a0020742

Observation aaedeca7-da18-4389-996e-139a69818b1f · outbound

This paper cites Learn2augment: learning to composite videos for data augmentation in ac- tion recognition.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Learn2augment: learning to composite videos for data augmentation in ac- tion recognition

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:37:23.603245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:37:16.563519Z digest=sha256:1955725b267e6d7ba07e6b961cb1f7cb5ce85cb3a43b68b4b2c97eeba1662841

Observation 9cfa774b-c41b-4853-96fb-8a535021a89f · outbound

This paper cites Instance-wise occlusion and depth orders in natural scenes.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation Instance-wise occlusion and depth orders in natural scenes

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:37:22.986685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:37:17.406906Z digest=sha256:65b72caff4cf3ae6a3dae70c5c5aed1d81a9ad291fff36a13e3436255c3c79b2

Observation bcfd0b05-4dc7-41cc-801b-84536be8fcc6 · outbound

This paper cites STA-V2A: Video-to-Audio Generation with Semantic and Temporal Alignment.

Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation STA-V2A: Video-to-Audio Generation with Semantic and Temporal Alignment

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-06T20:37:18.302787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:37:18.302787Z digest=sha256:4a82a66c49b590f7b924d305e2160bf63e7cf37dfd1dea2765738fbcc0e34637

Pith citing papers

No inbound Pith citation observations are available.