Paper Citation Record · LEDGER

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer

As of 9 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2506.23623.

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

pith.paper-citation-record.v1
2506.23623 v1

Coverage vector

measured 44 of 44 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

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Outbound references

Observation aa8f3d36-a08a-421c-8fdf-47fd3b0c4810 · outbound

This paper cites YouTube-8M: A Large-Scale Video Classification Benchmark.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer YouTube-8M: A Large-Scale Video Classification Benchmark

Reference 1

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Observation 23cd1ae3-f838-4f6e-bfd7-6a2f85723205 · outbound

This paper cites Look, listen and learn.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Look, listen and learn

Reference 2

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Observation b1e50aeb-48e1-43bd-bf45-e4008fa20df0 · outbound

This paper cites Objects that sound.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Objects that sound

Reference 3

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This paper cites End-to- end object detection with transformers.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer End-to- end object detection with transformers

Reference 4

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Observation e5201e53-e6e6-48ff-ad7b-cea907d754ce · outbound

This paper cites Unraveling in- stance associations: A closer look for audio-visual segmen- tation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Unraveling in- stance associations: A closer look for audio-visual segmen- tation

Reference 5

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This paper cites Cpm: Class-conditional prompting ma- chine for audio-visual segmentation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Cpm: Class-conditional prompting ma- chine for audio-visual segmentation

Reference 6

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This paper cites Masked-attention mask transformer for universal image segmentation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Masked-attention mask transformer for universal image segmentation

Reference 7

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This paper cites Imagenet: A large-scale hierarchical image database.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Imagenet: A large-scale hierarchical image database

Reference 8

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This paper cites Avsegformer: Audio-visual segmentation with trans- former.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Avsegformer: Audio-visual segmentation with trans- former

Reference 9

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This paper cites Improving audio-visual segmentation with bidirectional generation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Improving audio-visual segmentation with bidirectional generation

Reference 10

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This paper cites Deep residual learning for image recognition.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Deep residual learning for image recognition

Reference 11

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Observation ac069957-c7ae-472b-89a3-ad52f747f997 · outbound

This paper cites Cnn ar- chitectures for large-scale audio classification.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Cnn ar- chitectures for large-scale audio classification

Reference 12

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Observation 8c3f7eca-2434-46e9-aab8-232ffd9aca7b · outbound

This paper cites Discriminative sounding objects localization via self-supervised audiovisual match- ing.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Discriminative sounding objects localization via self-supervised audiovisual match- ing

Reference 13

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This paper cites Mix and local- ize: Localizing sound sources in mixtures.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Mix and local- ize: Localizing sound sources in mixtures

Reference 14

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Observation 40b57b97-ca7c-4cee-a191-ed46c83c5091 · outbound

This paper cites Discovering sound- ing objects by audio queries for audio visual segmentation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Discovering sound- ing objects by audio queries for audio visual segmentation

Reference 15

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Observation d25fb5fe-93bb-4656-9b93-1d1cff10c754 · outbound

This paper cites Unleashing the temporal-spatial reasoning capacity of gpt for training-free audio and language referenced video object segmentation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Unleashing the temporal-spatial reasoning capacity of gpt for training-free audio and language referenced video object segmentation

Reference 16

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This paper cites Categorical Reparameterization with Gumbel-Softmax.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Categorical Reparameterization with Gumbel-Softmax

Reference 17

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Observation 41ad0a72-2ffe-45a9-aa0f-8e4e1e4ddf6f · outbound

This paper cites Learning to visually localize sound sources from mixtures without prior source knowledge.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Learning to visually localize sound sources from mixtures without prior source knowledge

Reference 18

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Observation e0850671-1124-4d09-9b43-ad461b3e9f85 · outbound

This paper cites Segment and recognize anything at any granularity.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Segment and recognize anything at any granularity

Reference 19

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This paper cites Selm: Selective mechanism based audio-visual segmentation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Selm: Selective mechanism based audio-visual segmentation

Reference 20

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This paper cites Catr: Combinatorial-dependence audio-queried transformer for audio-visual video segmentation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Catr: Combinatorial-dependence audio-queried transformer for audio-visual video segmentation

Reference 21

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Observation bc2fa17b-80a6-4743-8d1d-ba532f346af0 · outbound

This paper cites Qdformer: Towards robust audiovisual segmentation in complex environments with quantization-based semantic decomposition.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Qdformer: Towards robust audiovisual segmentation in complex environments with quantization-based semantic decomposition

Reference 22

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Observation 99edfce9-9f0a-4e00-8f3a-90cd10fa66f3 · outbound

This paper cites Audio-visual seg- mentation by exploring cross-modal mutual semantics.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Audio-visual seg- mentation by exploring cross-modal mutual semantics

Reference 23

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This paper cites Bavs: bootstrapping audio- visual segmentation by integrating foundation knowledge.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Bavs: bootstrapping audio- visual segmentation by integrating foundation knowledge

Reference 24

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This paper cites Audio-visual segmentation via unlabeled frame exploitation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Audio-visual segmentation via unlabeled frame exploitation

Reference 25

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Swin transformer: Hierarchical vision transformer using shifted windows

Reference 26

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Observation f4a473fa-928b-4c7b-9581-f898b61efaf2 · outbound

This paper cites Step- ping stones: A progressive training strategy for audio-visual semantic segmentation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Step- ping stones: A progressive training strategy for audio-visual semantic segmentation

Reference 27

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This paper cites The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

Reference 28

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This paper cites Multimodal variational auto-encoder based audio-visual segmentation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Multimodal variational auto-encoder based audio-visual segmentation

Reference 29

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This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 30

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This paper cites Localizing visual sounds the easy way.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Localizing visual sounds the easy way

Reference 31

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This paper cites ImageNet-21K Pretraining for the Masses.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer ImageNet-21K Pretraining for the Masses

Reference 32

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

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Learning to localize sound source in visual scenes

Reference 33

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Observation 8558b1c3-c7cd-4b42-a45b-986f175d4a75 · outbound

This paper cites Unveiling and mitigating bias in audio visual segmentation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Unveiling and mitigating bias in audio visual segmentation

Reference 34

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This paper cites Attention is all you need.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Attention is all you need

Reference 35

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This paper cites Pvt v2: Improved baselines with pyramid vision transformer.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Pvt v2: Improved baselines with pyramid vision transformer

Reference 36

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This paper cites Prompting segmentation with sound is gen- eralizable audio-visual source localizer.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Prompting segmentation with sound is gen- eralizable audio-visual source localizer

Reference 37

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This paper cites Can textual semantics mitigate sounding object segmentation preference? In ECCV, 2024.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Can textual semantics mitigate sounding object segmentation preference? In ECCV, 2024

Reference 38

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This paper cites Language as queries for referring video object segmen- tation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Language as queries for referring video object segmen- tation

Reference 39

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This paper cites Groupvit: Semantic segmentation emerges from text supervision.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Groupvit: Semantic segmentation emerges from text supervision

Reference 40

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Observation 7b97016a-c9d5-4421-b092-c2d49bdb3fca · outbound

This paper cites Cooperation does matter: Exploring multi-order bilateral relations for audio- visual segmentation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Cooperation does matter: Exploring multi-order bilateral relations for audio- visual segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:41:36.625618Z

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This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Reference 42

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This paper cites Audio–visual segmentation.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Audio–visual segmentation

Reference 43

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Observation 0970f7ae-fe0a-4f3b-9879-55ca2b4724c6 · outbound

This paper cites Audio-visual segmentation with semantics.

Revisiting Audio-Visual Segmentation with Vision-Centric Transformer Audio-visual segmentation with semantics

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:41:36.314198Z

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Pith citing papers

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