RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer | Paper Citation Record

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This paper cites write newline.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer write newline

Reference 1

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This paper cites and Zisserman, A.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer and Zisserman, A

Reference 2

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This paper cites Singular value decomposition tutorial.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Singular value decomposition tutorial

Reference 3

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Observation 82bf73e3-e43b-46b8-8094-7ab82b49670d · outbound

This paper cites Multimodal machine learning: A survey and taxonomy.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Multimodal machine learning: A survey and taxonomy

Reference 4

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Observation 2cafc4cc-ed47-42e3-880b-9fcd5e97f458 · outbound

This paper cites Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters

Reference 5

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This paper cites G., Keutmann, M.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer G., Keutmann, M

Reference 6

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

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer End-to-end object detection with transformers

Reference 7

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

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Vggsound: A large-scale audio-visual dataset

Reference 8

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This paper cites T., Rubanova, Y., Bettencourt, J., and Duvenaud, D.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer T., Rubanova, Y., Bettencourt, J., and Duvenaud, D

Reference 9

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This paper cites Self-attention fusion for audiovisual emotion recognition with incomplete data.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Self-attention fusion for audiovisual emotion recognition with incomplete data

Reference 10

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This paper cites What Does BERT Look At? An Analysis of BERT's Attention.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer What Does BERT Look At? An Analysis of BERT's Attention

Reference 11

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This paper cites Addressing failure prediction by learning model confidence.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Addressing failure prediction by learning model confidence

Reference 12

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Observation 55f3978c-c448-4684-ae99-900215ee25cf · outbound

This paper cites MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities

Reference 13

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Observation b7bb59bc-732c-44d1-b58a-d335e21ac48a · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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This paper cites Pmr: Prototypical modal rebalance for multimodal learning.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Pmr: Prototypical modal rebalance for multimodal learning

Reference 15

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Observation 545a75d6-ae72-469f-a773-b66ee6fd6e5f · outbound

This paper cites A survey on deep learning for multimodal data fusion.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer A survey on deep learning for multimodal data fusion

Reference 16

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This paper cites C., Wang, X., and Li, H.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer C., Wang, X., and Li, H

Reference 17

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Observation 8820c915-2613-45bc-ac88-5c32f18a1484 · outbound

This paper cites A mathematical perspective on Transformers.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer A mathematical perspective on Transformers

Reference 18

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Observation 03b11aec-86b4-4fd2-9e88-275a6673e170 · outbound

This paper cites Multimodal dynamics: Dynamical fusion for trustworthy multimodal classification.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Multimodal dynamics: Dynamical fusion for trustworthy multimodal classification

Reference 19

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This paper cites Deep residual learning for image recognition.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Deep residual learning for image recognition

Reference 20

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This paper cites Masked autoencoders are scalable vision learners.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Masked autoencoders are scalable vision learners

Reference 21

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This paper cites ReconBoost: Boosting Can Achieve Modality Reconcilement.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer ReconBoost: Boosting Can Achieve Modality Reconcilement

Reference 22

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Observation 7aaf9aba-4f25-42eb-8a9f-d8ec915f37d2 · outbound

This paper cites Adaptive unimodal regulation for balanced multimodal information acquisition.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Adaptive unimodal regulation for balanced multimodal information acquisition

Reference 23

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This paper cites Modality competition: What makes joint training of multi-modal network fail in deep learning?(provably).

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Modality competition: What makes joint training of multi-modal network fail in deep learning?(provably)

Reference 24

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This paper cites Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers

Reference 25

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Unresolved cited work

Reference 26

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This paper cites Vilt: Vision-and-language transformer without convolution or region supervision.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Vilt: Vision-and-language transformer without convolution or region supervision

Reference 27

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This paper cites Revealing the Dark Secrets of BERT.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Revealing the Dark Secrets of BERT

Reference 28

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This paper cites Hmdb: a large video database for human motion recognition.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Hmdb: a large video database for human motion recognition

Reference 29

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer VisualBERT: A Simple and Performant Baseline for Vision and Language

Reference 30

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer P., Lyu, Y., Fan, X., Wu, Z., Cheng, Y., Wu, J., Chen, L., Wu, P., Lee, M

Reference 31

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This paper cites Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Reference 32

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Efficient Low-rank Multimodal Fusion with Modality-Specific Factors

Reference 33

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Attention bottlenecks for multimodal fusion

Reference 34

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Observation 239cea9e-0360-41c7-bea2-1287ac56cd59 · outbound

This paper cites Balanced multimodal learning via on-the-fly gradient modulation.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Balanced multimodal learning via on-the-fly gradient modulation

Reference 35

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This paper cites Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion

Reference 36

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Zorro: the masked multimodal transformer

Reference 37

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer ImageNet-21K Pretraining for the Masses

Reference 38

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer and Monro, S

Reference 39

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 40

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This paper cites L., Tickoo, O., and Huang, J.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer L., Tickoo, O., and Huang, J

Reference 41

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer H., Bai, S., Liang, P

Reference 42

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This paper cites Attention is all you need.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Attention is all you need

Reference 43

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer H., Zeeshan, M

Reference 44

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This paper cites What makes training multi-modal classification networks hard? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 12695--12705, 2020 a.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer What makes training multi-modal classification networks hard? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 12695--12705, 2020 a

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:47.029696Z

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This paper cites Deep multimodal fusion by channel exchanging.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Deep multimodal fusion by channel exchanging

Reference 46

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Multimodal token fusion for vision transformers

Reference 47

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Unresolved cited work

Reference 48

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Observation 7323a5ea-4aaa-4e4c-aa74-bd1a0b5851e2 · outbound

This paper cites Enhancing multimodal cooperation via sample-level modality valuation.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Enhancing multimodal cooperation via sample-level modality valuation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:46.014133Z

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Unresolved cited work

Reference 50

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This paper cites Multimodal fusion with co-attention networks for fake news detection.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Multimodal fusion with co-attention networks for fake news detection

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:45.527009Z

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This paper cites Multimodal multi-loss fusion network for sentiment analysis.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Multimodal multi-loss fusion network for sentiment analysis

Reference 52

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This paper cites Balanced Audiovisual Dataset for Imbalance Analysis.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Balanced Audiovisual Dataset for Imbalance Analysis

Reference 53

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Observation f9ef6733-d0c6-460b-9bd2-99b47c4c3490 · outbound

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RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Unresolved cited work

Reference 54

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Observation 66ed191c-5d3d-4682-beeb-b3e45e79ae60 · outbound

This paper cites Facilitating multimodal classification via dynamically learning modality gap.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Facilitating multimodal classification via dynamically learning modality gap

Reference 55

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This paper cites Learning to rebalance multi-modal optimization by adaptively masking subnetworks.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Learning to rebalance multi-modal optimization by adaptively masking subnetworks

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:44.656662Z

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This paper cites Deep modular co-attention networks for visual question answering.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Deep modular co-attention networks for visual question answering

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:44.419762Z

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This paper cites Tensor Fusion Network for Multimodal Sentiment Analysis.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Tensor Fusion Network for Multimodal Sentiment Analysis

Reference 58

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This paper cites B., Liang, P.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer B., Liang, P

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:44.221027Z

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This paper cites T., and Peng, X.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer T., and Peng, X

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:43.959333Z

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This paper cites Multimodal Fusion on Low-quality Data: A Comprehensive Survey.

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer Multimodal Fusion on Low-quality Data: A Comprehensive Survey

Reference 61

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