Principles of Visual Tokens for Efficient Video Understanding | Paper Citation Record

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This paper cites Vivit: A video vi- sion transformer.

Principles of Visual Tokens for Efficient Video Understanding Vivit: A video vi- sion transformer

Reference 1

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Observation c9348b78-6a67-4449-bb64-609cc30b4912 · outbound

This paper cites Is Space-Time Attention All You Need for Video Understanding?.

Principles of Visual Tokens for Efficient Video Understanding Is Space-Time Attention All You Need for Video Understanding?

Reference 2

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This paper cites Is space-time attention all you need for video understanding? In ICML, page 4, 2021.

Principles of Visual Tokens for Efficient Video Understanding Is space-time attention all you need for video understanding? In ICML, page 4, 2021

Reference 3

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This paper cites Token merging: Your ViT but faster.

Principles of Visual Tokens for Efficient Video Understanding Token merging: Your ViT but faster

Reference 4

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Observation 42b7f1f4-3f60-4fac-a742-73a4fe5dc6ef · outbound

This paper cites Revisiting the” video” in video-language understanding.

Principles of Visual Tokens for Efficient Video Understanding Revisiting the” video” in video-language understanding

Reference 5

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This paper cites Space-time mixing attention for video transformer.

Principles of Visual Tokens for Efficient Video Understanding Space-time mixing attention for video transformer

Reference 6

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This paper cites Activitynet: A large-scale video benchmark for human activity understanding.

Principles of Visual Tokens for Efficient Video Understanding Activitynet: A large-scale video benchmark for human activity understanding

Reference 7

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

Principles of Visual Tokens for Efficient Video Understanding Quo vadis, action recognition? a new model and the kinetics dataset

Reference 8

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This paper cites An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models, 2024.

Principles of Visual Tokens for Efficient Video Understanding An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models, 2024

Reference 9

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Principles of Visual Tokens for Efficient Video Understanding Unresolved cited work

Reference 10

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This paper cites Prune spatio-temporal tokens by semantic-aware temporal accumulation.

Principles of Visual Tokens for Efficient Video Understanding Prune spatio-temporal tokens by semantic-aware temporal accumulation

Reference 11

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This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

Principles of Visual Tokens for Efficient Video Understanding An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 12

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This paper cites Multiscale vision transformers.

Principles of Visual Tokens for Efficient Video Understanding Multiscale vision transformers

Reference 13

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This paper cites X3d: Expanding architectures for efficient video recognition.

Principles of Visual Tokens for Efficient Video Understanding X3d: Expanding architectures for efficient video recognition

Reference 14

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This paper cites Slowfast networks for video recognition.

Principles of Visual Tokens for Efficient Video Understanding Slowfast networks for video recognition

Reference 15

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This paper cites Efficient video transformers via spatial-temporal token merging for action recognition.

Principles of Visual Tokens for Efficient Video Understanding Efficient video transformers via spatial-temporal token merging for action recognition

Reference 16

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This paper cites Smart frame selection for action recognition.

Principles of Visual Tokens for Efficient Video Understanding Smart frame selection for action recognition

Reference 17

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This paper cites Watt For What: Rethinking Deep Learning's Energy-Performance Relationship.

Principles of Visual Tokens for Efficient Video Understanding Watt For What: Rethinking Deep Learning's Energy-Performance Relationship

Reference 18

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This paper cites Optimizing factorized encoder models: Time and memory reduction for scalable and efficient action recognition.

Principles of Visual Tokens for Efficient Video Understanding Optimizing factorized encoder models: Time and memory reduction for scalable and efficient action recognition

Reference 19

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Principles of Visual Tokens for Efficient Video Understanding something something

Reference 20

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This paper cites Ava: A video dataset of spatio-temporally localized atomic visual actions.

Principles of Visual Tokens for Efficient Video Understanding Ava: A video dataset of spatio-temporally localized atomic visual actions

Reference 21

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Principles of Visual Tokens for Efficient Video Understanding Unresolved cited work

Reference 22

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Observation cfa34fc8-2a72-4cc2-8ae8-ac1bf3f41028 · outbound

This paper cites LookupViT: Compressing visual information to a limited number of tokens.

Principles of Visual Tokens for Efficient Video Understanding LookupViT: Compressing visual information to a limited number of tokens

Reference 23

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Observation ecbcfb01-bc9f-473c-92a5-b1b7cdfe0ec4 · outbound

This paper cites Revisiting token pruning for object detection and instance segmentation.

Principles of Visual Tokens for Efficient Video Understanding Revisiting token pruning for object detection and instance segmentation

Reference 24

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This paper cites Swin transformer: 9 Hierarchical vision transformer using shifted windows.

Principles of Visual Tokens for Efficient Video Understanding Swin transformer: 9 Hierarchical vision transformer using shifted windows

Reference 25

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Observation 1340edae-8416-4d56-8865-ae5b6f5ede89 · outbound

This paper cites Video swin transformer.

Principles of Visual Tokens for Efficient Video Understanding Video swin transformer

Reference 26

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This paper cites Video transformer network.

Principles of Visual Tokens for Efficient Video Understanding Video transformer network

Reference 27

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This paper cites Expanding language-image pretrained models for gen- eral video recognition.

Principles of Visual Tokens for Efficient Video Understanding Expanding language-image pretrained models for gen- eral video recognition

Reference 28

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This paper cites St-adapter: Parameter-efficient image-to-video transfer learning.

Principles of Visual Tokens for Efficient Video Understanding St-adapter: Parameter-efficient image-to-video transfer learning

Reference 29

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This paper cites K-centered patch sampling for efficient video recognition.

Principles of Visual Tokens for Efficient Video Understanding K-centered patch sampling for efficient video recognition

Reference 30

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This paper cites Asano, Is- han Misra Florian Metze, Christoph Feichtenhofer, Andrea Vedaldi, and Jo ˜ao F.

Principles of Visual Tokens for Efficient Video Understanding Asano, Is- han Misra Florian Metze, Christoph Feichtenhofer, Andrea Vedaldi, and Jo ˜ao F

Reference 31

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This paper cites So, Maud Texier, and Jeff Dean.

Principles of Visual Tokens for Efficient Video Understanding So, Maud Texier, and Jeff Dean

Reference 32

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This paper cites How does the primate brain combine generative and discriminative computations in vision?.

Principles of Visual Tokens for Efficient Video Understanding How does the primate brain combine generative and discriminative computations in vision?

Reference 33

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This paper cites Dynamicvit: Efficient vision transformers with dynamic token sparsification.

Principles of Visual Tokens for Efficient Video Understanding Dynamicvit: Efficient vision transformers with dynamic token sparsification

Reference 34

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This paper cites TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?.

Principles of Visual Tokens for Efficient Video Understanding TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?

Reference 35

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This paper cites Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Ba- tra.

Principles of Visual Tokens for Efficient Video Understanding Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Ba- tra

Reference 36

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

Principles of Visual Tokens for Efficient Video Understanding Only time can tell: Discovering temporal data for temporal modeling

Reference 37

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

Principles of Visual Tokens for Efficient Video Understanding UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 38

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

Principles of Visual Tokens for Efficient Video Understanding VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

Reference 39

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This paper cites Training data-efficient image transformers & distillation through at- tention.

Principles of Visual Tokens for Efficient Video Understanding Training data-efficient image transformers & distillation through at- tention

Reference 40

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This paper cites Attention is all you need.

Principles of Visual Tokens for Efficient Video Understanding Attention is all you need

Reference 41

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This paper cites Efficient video transformers with spatial- temporal token selection.

Principles of Visual Tokens for Efficient Video Understanding Efficient video transformers with spatial- temporal token selection

Reference 42

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This paper cites Actionclip: Adapting language-image pretrained models for video action recognition.

Principles of Visual Tokens for Efficient Video Understanding Actionclip: Adapting language-image pretrained models for video action recognition

Reference 43

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This paper cites Vila: Efficient video-language alignment for video question answering.

Principles of Visual Tokens for Efficient Video Understanding Vila: Efficient video-language alignment for video question answering

Reference 44

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This paper cites Video-focalnets: Spatio-temporal focal modu- lation for video action recognition.

Principles of Visual Tokens for Efficient Video Understanding Video-focalnets: Spatio-temporal focal modu- lation for video action recognition

Reference 45

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This paper cites Manmatha, Alex Smola, and Philipp Kr¨ahenb¨uhl.

Principles of Visual Tokens for Efficient Video Understanding Manmatha, Alex Smola, and Philipp Kr¨ahenb¨uhl

Reference 46

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This paper cites Memvit: Memory-augmented multiscale vision transformer for efficient long-term video recognition.

Principles of Visual Tokens for Efficient Video Understanding Memvit: Memory-augmented multiscale vision transformer for efficient long-term video recognition

Reference 47

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This paper cites Can i trust your answer? visually grounded video question answering.

Principles of Visual Tokens for Efficient Video Understanding Can i trust your answer? visually grounded video question answering

Reference 48

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This paper cites Aim: Adapting image models for effi- cient video action recognition.

Principles of Visual Tokens for Efficient Video Understanding Aim: Adapting image models for effi- cient video action recognition

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:38:10.271496Z

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This paper cites A-vit: Adap- tive tokens for efficient vision transformer.

Principles of Visual Tokens for Efficient Video Understanding A-vit: Adap- tive tokens for efficient vision transformer

Reference 50

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Observation a7ba7476-5d2f-4e15-8c85-82037d865916 · outbound

This paper cites Self-chained image-language model for video localization and question answering.

Principles of Visual Tokens for Efficient Video Understanding Self-chained image-language model for video localization and question answering

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:38:10.235125Z

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This paper cites Pyramid feature attention net- work for saliency detection.

Principles of Visual Tokens for Efficient Video Understanding Pyramid feature attention net- work for saliency detection

Reference 52

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Observation c2c53224-c922-44fb-bbfc-34ab9845defc · outbound

This paper cites How can objects help action recognition? 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2353–2362, 2023.

Principles of Visual Tokens for Efficient Video Understanding How can objects help action recognition? 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2353–2362, 2023

Reference 53

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Observation 95ba41bf-3a83-443d-8bac-6c62795b021e · outbound

This paper cites ECO: Efficient Convolutional Network for Online Video Understanding.

Principles of Visual Tokens for Efficient Video Understanding ECO: Efficient Convolutional Network for Online Video Understanding

Reference 54

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