MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition | Paper Citation Record

Observation d0566584-8b1a-4e89-bc30-b1d8a7a49a6e · outbound

This paper cites Vivit: A video vision transformer.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Vivit: A video vision transformer

Reference 1

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Observation 18a8fd3e-b682-462a-913c-6f3ea3a4878d · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition BEiT: BERT Pre-Training of Image Transformers

Reference 2

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This paper cites Is space-time attention all you need for video under- standing? In Proceedings of the International Confer- ence on Machine Learning (ICML), July 2021.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Is space-time attention all you need for video under- standing? In Proceedings of the International Confer- ence on Machine Learning (ICML), July 2021

Reference 3

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Observation 0dd5bef8-715c-41db-a180-87c6bea1eb99 · outbound

This paper cites Coyo-700m: Image-text pair dataset, 2022.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Coyo-700m: Image-text pair dataset, 2022

Reference 4

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Observation 066d303e-31f0-495a-a9d5-e53f30ab3fcf · outbound

This paper cites Quo vadis, ac- tion recognition? a new model and the kinetics dataset.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Quo vadis, ac- tion recognition? a new model and the kinetics dataset

Reference 5

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Observation 6b1b048b-180e-4829-9f5b-7141258a6a96 · outbound

This paper cites Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding

Reference 6

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Observation 745bc955-cd20-424a-bb12-2e2c8673636e · outbound

This paper cites A simple framework for con- trastive learning of visual representations, 2020.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition A simple framework for con- trastive learning of visual representations, 2020

Reference 7

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Observation 35f85598-e66d-4960-b93e-a9b3d5b956d3 · outbound

This paper cites Feature-wise transformations.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Feature-wise transformations

Reference 8

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Observation ae2ebe95-7d61-4550-8588-d8c48b4c95d4 · outbound

This paper cites Multiscale vision transformers.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Multiscale vision transformers

Reference 9

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Observation 56401364-3da8-488c-ad07-e74ededb08f6 · outbound

This paper cites X3d: Expanding architec- tures for efficient video recognition.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition X3d: Expanding architec- tures for efficient video recognition

Reference 10

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Observation 2cfc5cce-ae53-43a3-9dfa-93799361f043 · outbound

This paper cites Masked Autoencoders As Spatiotemporal Learners.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Masked Autoencoders As Spatiotemporal Learners

Reference 11

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Observation c259ede9-b86a-4879-9f91-ae3ec10321ff · outbound

This paper cites Slowfast networks for video recog- nition.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Slowfast networks for video recog- nition

Reference 12

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Observation 5da590e6-82da-4603-8c2e-6b59e9ac8884 · outbound

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

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition The” something something” video database for learning and evaluating visual common sense

Reference 13

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Observation 68690598-06ab-4175-870e-f52779ee66e8 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 14

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Observation 39b35f9b-124f-4e9d-90f8-3ea5f91027d2 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Efficiently Modeling Long Sequences with Structured State Spaces

Reference 15

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Observation 70f67fe2-2691-497b-a62c-b808bbe5a4b0 · outbound

This paper cites Open-vocabulary object detection via vision and language knowledge distillation.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Open-vocabulary object detection via vision and language knowledge distillation

Reference 16

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Observation d3fe1164-933b-4afb-abab-edf756748487 · outbound

This paper cites Trustworthy machine learning: From data to models.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Trustworthy machine learning: From data to models

Reference 17

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Observation 245cfa19-8e54-4bed-a6b5-431633cdeba3 · outbound

This paper cites Turbo training with token dropout.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Turbo training with token dropout

Reference 18

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Observation 75dbef3c-98ee-4f7e-9e9e-9b8c55438318 · outbound

This paper cites Learning spatio-temporal features with 3d residual networks for action recognition.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Learning spatio-temporal features with 3d residual networks for action recognition

Reference 19

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Observation fd048668-ac8d-4cbe-87be-bcb5b743aee7 · outbound

This paper cites MambaVision: A Hybrid Mamba-Transformer Vision Backbone.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition MambaVision: A Hybrid Mamba-Transformer Vision Backbone

Reference 20

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Observation a4995629-e6d5-4ab9-a680-a5d3da6ba861 · outbound

This paper cites Clipscore: A reference- free evaluation metric for image captioning.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Clipscore: A reference- free evaluation metric for image captioning

Reference 21

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Observation afba095f-b5d7-4a4d-a56f-5b12f2cf32f8 · outbound

This paper cites Arbitrary style trans- fer in real-time with adaptive instance normalization.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Arbitrary style trans- fer in real-time with adaptive instance normalization

Reference 22

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Observation 250059a2-427d-4720-9901-4c9cb5b8384e · outbound

This paper cites VideoGraph: Recognizing Minutes-Long Human Activities in Videos.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition VideoGraph: Recognizing Minutes-Long Human Activities in Videos

Reference 23

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Observation 75807e7a-5368-46d2-982f-815c6503e810 · outbound

This paper cites Smeulders.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Smeulders

Reference 24

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Observation 5c017a9e-706f-40cf-af86-3ef5a43b02f3 · outbound

This paper cites Long movie clip classification with state-space video mod- els.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Long movie clip classification with state-space video mod- els

Reference 25

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Observation 034efcb6-bfc0-4177-9cbf-e9149da6fe12 · outbound

This paper cites Scaling up visual and vision- language representation learning with noisy text su- pervision.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Scaling up visual and vision- language representation learning with noisy text su- pervision

Reference 26

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Observation 06acb9ba-58e8-401a-b5ea-eddbfbff387e · outbound

This paper cites Laine, and Timo Aila.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Laine, and Timo Aila

Reference 27

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Observation c231f89a-87ef-43fe-b2bd-7cd483c811f4 · outbound

This paper cites The Kinetics Human Action Video Dataset.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition The Kinetics Human Action Video Dataset

Reference 28

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Observation 4e4c4956-8e4e-498e-9b53-449f2cedceab · outbound

This paper cites Kuehne, H.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Kuehne, H

Reference 29

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Observation 8821f97d-59ee-4773-9bee-8fc6a466d9c6 · outbound

This paper cites The lan- guage of actions: Recovering the syntax and semantics of goal-directed human activities.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition The lan- guage of actions: Recovering the syntax and semantics of goal-directed human activities

Reference 30

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Observation 11c6d7db-7c34-4bcf-985a-a185bbf26bb1 · outbound

This paper cites F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models

Reference 31

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Observation 965818a5-d127-4beb-b212-05a651f0ad89 · outbound

This paper cites Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering

Reference 32

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Observation 6c983faf-b075-426a-beb7-3bd8197b733e · outbound

This paper cites Videomamba: State space model for efficient video understanding, 2024.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Videomamba: State space model for efficient video understanding, 2024

Reference 33

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Observation 02fcb1cf-32a1-4da0-b55e-7c84cb0018dc · outbound

This paper cites Unmasked teacher: Towards training-efficient video foundation models.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Unmasked teacher: Towards training-efficient video foundation models

Reference 34

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Observation 27f5fb1b-f812-4cce-97ce-0a366490a1c7 · outbound

This paper cites Uniformer: Uni- fied transformer for efficient spatial-temporal repre- sentation learning.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Uniformer: Uni- fied transformer for efficient spatial-temporal repre- sentation learning

Reference 35

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Observation 26abb7e7-33e8-4293-b6cf-5b079c84c844 · outbound

This paper cites Mvitv2: Improved multiscale vision transformers for classification and detection.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Mvitv2: Improved multiscale vision transformers for classification and detection

Reference 36

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Observation 1141e522-dbbe-4950-b689-f4c2bf95f6ed · outbound

This paper cites Fo- caldreamer: Text-driven 3d editing via focal-fusion assembly.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Fo- caldreamer: Text-driven 3d editing via focal-fusion assembly

Reference 37

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Observation 83f7f27f-ffb3-4433-80db-6750c1eb9ce2 · outbound

This paper cites Pointmamba: A simple state space model for point cloud analysis.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Pointmamba: A simple state space model for point cloud analysis

Reference 38

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Observation 096c446c-94c6-459f-8729-50371225bc84 · outbound

This paper cites Jamba: A Hybrid Transformer-Mamba Language Model.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Jamba: A Hybrid Transformer-Mamba Language Model

Reference 39

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This paper cites Learning to recognize procedural activities with dis- tant supervision.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Learning to recognize procedural activities with dis- tant supervision

Reference 40

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Observation 82ff3b0f-f212-4e6f-88d6-b2b3490609bf · outbound

This paper cites Frozen CLIP Models are Efficient Video Learners.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Frozen CLIP Models are Efficient Video Learners

Reference 41

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Observation 9898fcdc-ced0-4e8e-9241-e170e65e6353 · outbound

This paper cites Annotation-free Audio-Visual Segmentation.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Annotation-free Audio-Visual Segmentation

Reference 42

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This paper cites VMamba: Visual State Space Model.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition VMamba: Visual State Space Model

Reference 43

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Observation 3b47763b-25ec-4a8e-bbc0-c867acec771c · outbound

This paper cites Video swin transformer.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Video swin transformer

Reference 44

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Observation 6170fa3d-63f0-4201-b1f0-b538fecb1aa3 · outbound

This paper cites Freesegdiff: Annotation-free saliency segmentation with diffusion models.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Freesegdiff: Annotation-free saliency segmentation with diffusion models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:09.524175Z

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Observation 03621015-443a-4b08-92ce-7b5cb1f70656 · outbound

This paper cites DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery

Reference 46

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Observation e52b8b84-b3f9-4342-bf72-92861215bdd6 · outbound

This paper cites Open-vocabulary semantic segmenta- tion with frozen vision-language models.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Open-vocabulary semantic segmenta- tion with frozen vision-language models

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:09.336866Z

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Observation 69c49ab9-c49e-4727-87ca-5df1356d7e02 · outbound

This paper cites Attrseg: open- vocabulary semantic segmentation via attribute decomposition-aggregation.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Attrseg: open- vocabulary semantic segmentation via attribute decomposition-aggregation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:09.120762Z

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Observation 2573ff8e-3f54-4079-b207-f54ad18e4c47 · outbound

This paper cites Scaling open-vocabulary object detection.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Scaling open-vocabulary object detection

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:07.890235Z

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Observation 46177889-2581-457d-bd97-0e86f0a016ed · outbound

This paper cites ClipCap: CLIP Prefix for Image Captioning.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition ClipCap: CLIP Prefix for Image Captioning

Reference 50

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Observation 8bad67b6-8264-424e-98ae-4d4ea9723705 · outbound

This paper cites Expanding Language-Image Pretrained Models for General Video Recognition.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Expanding Language-Image Pretrained Models for General Video Recognition

Reference 51

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Observation 00a9a893-7320-4bb5-884a-67d9c6c1167c · outbound

This paper cites Text-Only Training for Image Captioning using Noise-Injected CLIP.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Text-Only Training for Image Captioning using Noise-Injected CLIP

Reference 52

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Observation 1fceb07c-de6f-4b34-9b3b-72af3549bc11 · outbound

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MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Unresolved cited work

Reference 53

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Observation 600a5af7-0e65-45fa-9ef4-dc11436ed1f6 · outbound

This paper cites ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning

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Resolution: verified exact; local_arxiv, observed 2026-08-06T21:52:00.638728Z

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Observation 47ad84a8-cfe1-489d-8460-5ab7d35ae11f · outbound

This paper cites MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging

Reference 55

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This paper cites Dual-path adaptation from image to video transform- ers.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Dual-path adaptation from image to video transform- ers

Reference 56

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MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Peebles and Saining Xie

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:06.766762Z

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Observation e3362da8-a59f-4667-bb1e-fe3b44df7dbd · outbound

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MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Unresolved cited work

Reference 58

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Observation 09d3aab8-921b-417a-ba0d-7e92b0719a3c · outbound

This paper cites Dis- entangling spatial and temporal learning for efficient image-to-video transfer learning.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Dis- entangling spatial and temporal learning for efficient image-to-video transfer learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:06.295700Z

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Observation 4c805022-7f59-4adb-be59-353adf757540 · outbound

This paper cites Learning transferable visual models from natural lan- guage supervision.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Learning transferable visual models from natural lan- guage supervision

Reference 60

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Observation ead28b01-3e89-46be-8e2f-ef446ded4382 · outbound

This paper cites Token- learner: Adaptive space-time tokenization for videos.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Token- learner: Adaptive space-time tokenization for videos

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:05.975865Z

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Observation 7c17898b-796b-46d0-afdd-8836a651f2b1 · outbound

This paper cites LAION-5B: An open large-scale dataset for training next generation image-text models.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition LAION-5B: An open large-scale dataset for training next generation image-text models

Reference 62

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Observation 8b354a31-49bd-4357-9a09-10a6cd7e5e34 · outbound

This paper cites Only time can tell: Discovering temporal data for temporal modeling.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Only time can tell: Discovering temporal data for temporal modeling

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:05.790063Z

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Observation 8c0e1250-f422-4fe9-9601-1a08ccdc984d · outbound

This paper cites Darf: Depth- aware generalizable neural radiance field.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Darf: Depth- aware generalizable neural radiance field

Reference 64

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Observation 28917599-655e-4971-8114-058987c3fdcb · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 65

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This paper cites Coin: A large-scale dataset for comprehensive instruc- tional video analysis.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Coin: A large-scale dataset for comprehensive instruc- tional video analysis

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:05.432865Z

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Observation fbdc6a50-7eec-48f7-8c45-b34de633b22f · outbound

This paper cites Dim: Diffusion mamba for efficient high-resolution image synthesis, 2024.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Dim: Diffusion mamba for efficient high-resolution image synthesis, 2024

Reference 67

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Observation af449d3e-434b-4f99-ada6-63a5585c1387 · outbound

This paper cites VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

Reference 68

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Observation 0c40be8d-6286-4410-9506-79a9f25f2837 · outbound

This paper cites An Empirical Study of Mamba-based Language Models.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition An Empirical Study of Mamba-based Language Models

Reference 69

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Observation a3092b77-db40-4209-bc01-513dabb0a114 · outbound

This paper cites Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation

Reference 70

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Observation fefb174e-7ad2-4264-838b-62483d589634 · outbound

This paper cites ActionCLIP: A New Paradigm for Video Action Recognition.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition ActionCLIP: A New Paradigm for Video Action Recognition

Reference 71

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Observation 897aba94-bd40-4570-a160-c81813c23320 · outbound

This paper cites Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

Reference 72

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Observation 6cdd6186-d78a-4fba-8888-cf116c13b917 · outbound

This paper cites PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition PoinTramba: A Hybrid Transformer-Mamba Framework for Point Cloud Analysis

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T21:51:58.110217Z

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Observation 94f0fbf7-8e97-43b8-a1e3-e50cb9b47a84 · outbound

This paper cites DualNeRF: Text-Driven 3D Scene Editing via Dual-Field Representation.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition DualNeRF: Text-Driven 3D Scene Editing via Dual-Field Representation

Reference 74

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Observation 7d526405-4e77-4079-a62e-68b17786e9ee · outbound

This paper cites CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment

Reference 75

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Observation 90e734f1-29e5-4dc2-8f66-3a7369f14b76 · outbound

This paper cites Multiview transformers for video recognition.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Multiview transformers for video recognition

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:03.304085Z

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Observation 68feddfa-c382-4fae-bb7d-49084f20ac09 · outbound

This paper cites MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology

Reference 77

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Observation f2ed0136-122a-4a41-902c-67b430d24944 · outbound

This paper cites AIM: Adapting image mod- els for efficient video action recognition.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition AIM: Adapting image mod- els for efficient video action recognition

Reference 78

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Observation 8c6e1585-036c-4a36-987c-68a3ef9dc655 · outbound

This paper cites Multi- modal prototypes for open-world semantic segmen- tation.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Multi- modal prototypes for open-world semantic segmen- tation

Reference 79

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Observation f07b56a4-579a-47ed-ad1b-744a7fafac25 · outbound

This paper cites Remamber: Referring image segmentation with mamba twister.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Remamber: Referring image segmentation with mamba twister

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:02.772877Z

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Observation c229ec09-62d6-4d04-b8e3-a67848f1fee9 · outbound

This paper cites Learning with multi- class auc: Theory and algorithms.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Learning with multi- class auc: Theory and algorithms

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:02.585717Z

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Observation 9a409808-e05f-4f5c-a661-b69f11de62a6 · outbound

This paper cites Optimizing two-way partial auc with an end-to-end framework.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Optimizing two-way partial auc with an end-to-end framework

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:02.417014Z

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Observation 7cbfadaf-943e-4673-8f37-e4d2aad83fb1 · outbound

This paper cites Sigmoid Loss for Language Image Pre-Training.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Sigmoid Loss for Language Image Pre-Training

Reference 83

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Observation c448c7b1-0717-4d7e-baff-928868910d1e · outbound

This paper cites Point Cloud Mamba: Point Cloud Learning via State Space Model.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Point Cloud Mamba: Point Cloud Learning via State Space Model

Reference 84

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Observation 613a4a46-2840-47e1-a6a4-eb0402e8d530 · outbound

This paper cites G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models

Reference 85

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Observation c101d2c5-237a-4331-8e65-f43b9565ab7e · outbound

This paper cites Vidtr: Video transformer without convo- lutions.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Vidtr: Video transformer without convo- lutions

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:02.118114Z

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Observation 8014a4aa-3a2c-4def-8347-74f7b1beac90 · outbound

This paper cites Regionclip: Region-based language-image pretraining.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Regionclip: Region-based language-image pretraining

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:01.837632Z

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Observation 6deeac2c-3a55-4d5d-8cb7-97eb4bc75e57 · outbound

This paper cites Graph-based high-order relation modeling for long-term action recognition.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Graph-based high-order relation modeling for long-term action recognition

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:01.571641Z

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Observation ce255339-f313-4b06-8e78-b21533f4e672 · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 89

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