GViT: Representing Images as Gaussians for Visual Recognition | Paper Citation Record

Observation 9945d05e-7020-4d1e-9ab0-bd728522523a · outbound

This paper cites Slic superpixels compared to state-of-the-art superpixel methods.

GViT: Representing Images as Gaussians for Visual Recognition Slic superpixels compared to state-of-the-art superpixel methods

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:18.892758Z

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Observation 2c890d52-e762-428e-9657-dad3cf888b27 · outbound

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

GViT: Representing Images as Gaussians for Visual Recognition BEiT: BERT Pre-Training of Image Transformers

Reference 2

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GViT: Representing Images as Gaussians for Visual Recognition Unresolved cited work

Reference 3

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Observation 5910e295-c639-4bc8-80ed-03a55f33dc01 · outbound

This paper cites Class-Discriminative Attention Maps for Vision Transformers.

GViT: Representing Images as Gaussians for Visual Recognition Class-Discriminative Attention Maps for Vision Transformers

Reference 4

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Observation fd5ef612-735c-428c-a5a7-dfeddb721ae2 · outbound

This paper cites Generative pretraining from pixels.

GViT: Representing Images as Gaussians for Visual Recognition Generative pretraining from pixels

Reference 5

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Observation 936e8e54-4ae0-45ba-b2b1-bb233b9fc377 · outbound

This paper cites ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators.

GViT: Representing Images as Gaussians for Visual Recognition ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Reference 6

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Observation f4c5c823-5540-4b5a-b51a-db3931724776 · outbound

This paper cites Vision transformers need registers.

GViT: Representing Images as Gaussians for Visual Recognition Vision transformers need registers

Reference 7

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Observation 37e73944-bae5-47a0-860c-c20c99361830 · outbound

This paper cites Scaling vision transformers to 22 billion parameters.

GViT: Representing Images as Gaussians for Visual Recognition Scaling vision transformers to 22 billion parameters

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T21:47:11.395995Z

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Observation fdd7961f-9de7-4c5f-be6a-aead69cc1528 · outbound

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

GViT: Representing Images as Gaussians for Visual Recognition An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T21:47:11.450427Z

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Observation 9c3db3c5-a6d1-4b5d-ba0d-e706919159f9 · outbound

This paper cites Adaptive slot attention: Object discovery with dynamic slot number.

GViT: Representing Images as Gaussians for Visual Recognition Adaptive slot attention: Object discovery with dynamic slot number

Reference 10

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Observation f66e188e-2881-48ee-bffe-e431d0f62f50 · outbound

This paper cites 3d gaussian splatting as new era: A survey.

GViT: Representing Images as Gaussians for Visual Recognition 3d gaussian splatting as new era: A survey

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:18.364413Z

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Observation c1fc16db-617d-46d9-86a7-162eed514be1 · outbound

This paper cites Efficient graph-based image segmentation.

GViT: Representing Images as Gaussians for Visual Recognition Efficient graph-based image segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:18.238363Z

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Observation 579af9de-e56a-451e-b5b9-2714f0311f26 · outbound

This paper cites Understanding the difficulty of training deep feedfor- ward neural networks.

GViT: Representing Images as Gaussians for Visual Recognition Understanding the difficulty of training deep feedfor- ward neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:18.119540Z

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Observation ff0bb9f3-9761-48f2-affb-e7a0de90b98a · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

GViT: Representing Images as Gaussians for Visual Recognition Explaining and Harnessing Adversarial Examples

Reference 14

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Observation b192aebd-5bf7-4416-977f-feba17913d7e · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

GViT: Representing Images as Gaussians for Visual Recognition Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 15

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This paper cites Faster neural networks straight from jpeg.

GViT: Representing Images as Gaussians for Visual Recognition Faster neural networks straight from jpeg

Reference 16

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Observation dc0b8bf2-6a07-4b45-a0b1-4398de97033a · outbound

This paper cites Masked autoencoders are scalable vision learners.

GViT: Representing Images as Gaussians for Visual Recognition Masked autoencoders are scalable vision learners

Reference 17

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Observation 261a297a-38f0-42d1-a87a-84055dd8365c · outbound

This paper cites Deep residual learning for im- age recognition.

GViT: Representing Images as Gaussians for Visual Recognition Deep residual learning for im- age recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:17.880417Z

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Observation 846f3813-b848-4d05-990d-3c5ce50e77ea · outbound

This paper cites Bytes are all you need: Transformers operating directly on file bytes.

GViT: Representing Images as Gaussians for Visual Recognition Bytes are all you need: Transformers operating directly on file bytes

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:17.757233Z

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Observation 200ecfb6-b37f-43f2-9086-224e9134a633 · outbound

This paper cites 2d gaussian splatting for geometrically accurate radiance fields.

GViT: Representing Images as Gaussians for Visual Recognition 2d gaussian splatting for geometrically accurate radiance fields

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:17.643782Z

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Observation fa411bc1-4037-4db5-8286-9e0aeb480988 · outbound

This paper cites Perceiver IO: A General Architecture for Structured Inputs & Outputs.

GViT: Representing Images as Gaussians for Visual Recognition Perceiver IO: A General Architecture for Structured Inputs & Outputs

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:17.543035Z

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Observation 8b67a148-fc63-4ce8-97dd-954a45b029db · outbound

This paper cites Perceiver IO: A general architecture for structured inputs & outputs.

GViT: Representing Images as Gaussians for Visual Recognition Perceiver IO: A general architecture for structured inputs & outputs

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:17.398396Z

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Observation b1e8f434-2fcf-4c5b-a4b2-4ecf418d0a53 · outbound

This paper cites Perceiver: General Perception with Iterative Attention.

GViT: Representing Images as Gaussians for Visual Recognition Perceiver: General Perception with Iterative Attention

Reference 23

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Observation ea2f901f-f3cd-4cd1-80f6-79f9c737a85f · outbound

This paper cites Superpixel sampling networks.

GViT: Representing Images as Gaussians for Visual Recognition Superpixel sampling networks

Reference 24

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Observation 3738d5c8-68ff-4a31-be72-2ea36e1b9ea0 · outbound

This paper cites Unsupervised image segmentation by backpropagation.

GViT: Representing Images as Gaussians for Visual Recognition Unsupervised image segmentation by backpropagation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:17.066064Z

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Observation 56d5519e-e377-4930-93b9-f89ca3ee78ca · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.

GViT: Representing Images as Gaussians for Visual Recognition 3d gaussian splatting for real-time radiance field rendering

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:16.962101Z

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Observation a6efc2d2-c4bc-4755-a961-289d45fa081b · outbound

This paper cites Seac and the start of image processing at the national bureau of standards.

GViT: Representing Images as Gaussians for Visual Recognition Seac and the start of image processing at the national bureau of standards

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:16.825354Z

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GViT: Representing Images as Gaussians for Visual Recognition Unresolved cited work

Reference 28

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Observation 6ba2d68d-75c3-4501-8c4f-b387e64c880e · outbound

This paper cites Kutulakos, David J.

GViT: Representing Images as Gaussians for Visual Recognition Kutulakos, David J

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:16.415159Z

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Observation a6cba2a0-19ec-475c-a0c7-e0cc0eefc094 · outbound

This paper cites PyTorch Distributed: Experiences on Accelerating Data Parallel Training.

GViT: Representing Images as Gaussians for Visual Recognition PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Reference 30

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Observation 2c815d31-fe80-4758-a764-4601fe534137 · outbound

This paper cites A convnet for the 2020s.

GViT: Representing Images as Gaussians for Visual Recognition A convnet for the 2020s

Reference 31

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Observation bdd6e5d4-4c34-4d8b-9461-9dcca0ac6c66 · outbound

This paper cites Object-centric learning with slot attention.

GViT: Representing Images as Gaussians for Visual Recognition Object-centric learning with slot attention

Reference 32

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Source-reported events for the cited work

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Observation d926a667-08f5-4c40-bb5b-b0e508e55a00 · outbound

This paper cites Sgdr: Stochastic gradient descent with warm restarts.

GViT: Representing Images as Gaussians for Visual Recognition Sgdr: Stochastic gradient descent with warm restarts

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:16.093469Z

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Observation 30cabcaf-6846-453f-a2e5-9f7095397d4b · outbound

This paper cites Decoupled Weight Decay Regularization.

GViT: Representing Images as Gaussians for Visual Recognition Decoupled Weight Decay Regularization

Reference 34

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Observation f9c6bfc4-7a58-4297-a4ed-650674a643db · outbound

This paper cites Enhance the Visual Representation via Discrete Adversarial Training.

GViT: Representing Images as Gaussians for Visual Recognition Enhance the Visual Representation via Discrete Adversarial Training

Reference 35

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Observation cf18a7bb-9b6b-4674-8f32-bc1ff44f1e3c · outbound

This paper cites An image is worth more than 16x16 patches: Exploring transformers on individual pixels.

GViT: Representing Images as Gaussians for Visual Recognition An image is worth more than 16x16 patches: Exploring transformers on individual pixels

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:15.980100Z

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Observation 42d1f6b9-685f-462f-9500-ad2e411fcf97 · outbound

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GViT: Representing Images as Gaussians for Visual Recognition Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:47:15.875334Z

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Observation e517d215-d6b7-45cc-b96d-efacca6aeec9 · outbound

This paper cites Rgb no more: Minimally-decoded jpeg vision transformers.

GViT: Representing Images as Gaussians for Visual Recognition Rgb no more: Minimally-decoded jpeg vision transformers

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:15.695604Z

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Observation 51ff19dd-a76a-45ab-9c72-8799982fd377 · outbound

This paper cites Gaussian Masked Autoencoders.

GViT: Representing Images as Gaussians for Visual Recognition Gaussian Masked Autoencoders

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:47:14.169384Z

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Observation 326b0d3b-41f2-4a27-9b57-1129e725fce4 · outbound

This paper cites Learning a classification model for segmentation.

GViT: Representing Images as Gaussians for Visual Recognition Learning a classification model for segmentation

Reference 40

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Observation 0836e8a5-4ef1-445b-bacb-576c6956ee30 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

GViT: Representing Images as Gaussians for Visual Recognition Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 41

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Observation c0ea71a1-20eb-4917-9fc1-5c0ec8c287e8 · outbound

This paper cites How to train your vit? data, augmentation, and regularization in vision transformers.

GViT: Representing Images as Gaussians for Visual Recognition How to train your vit? data, augmentation, and regularization in vision transformers

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:15.305740Z

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Observation a4d8a551-bbbe-44e0-a939-83fbef92fc93 · outbound

This paper cites Superpixels: An evaluation of the state-of-the-art.

GViT: Representing Images as Gaussians for Visual Recognition Superpixels: An evaluation of the state-of-the-art

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T21:47:13.212064Z

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Observation 5340c48a-b528-4227-a4de-b16e52df9857 · outbound

This paper cites Single-view view synthesis with multiplane images.

GViT: Representing Images as Gaussians for Visual Recognition Single-view view synthesis with multiplane images

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:15.139091Z

Source-reported events for the cited work

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Observation 635841d9-9702-4ce9-8f0c-7eb7efbe101f · outbound

This paper cites Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.

GViT: Representing Images as Gaussians for Visual Recognition Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T21:47:13.279370Z

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Observation ffd14c20-ccef-4ae2-ab4f-20b68397693d · outbound

This paper cites Matching networks for one shot learning.

GViT: Representing Images as Gaussians for Visual Recognition Matching networks for one shot learning

Reference 46

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Observation e1d282c4-f13d-41c1-801a-ac2fb39a08f0 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

GViT: Representing Images as Gaussians for Visual Recognition Image quality assessment: from error visibility to structural similarity

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T21:47:13.357382Z

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Observation 06c8f80a-1f85-441c-86cf-7007377b6e37 · outbound

This paper cites Beyond Language Models: Byte Models are Digital World Simulators.

GViT: Representing Images as Gaussians for Visual Recognition Beyond Language Models: Byte Models are Digital World Simulators

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:47:14.007426Z

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Observation 5135e461-daef-48a1-bcdd-0af3d01951a0 · outbound

This paper cites Learning in the frequency domain.

GViT: Representing Images as Gaussians for Visual Recognition Learning in the frequency domain

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:14.983179Z

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Observation c8e13307-e537-4105-9de6-390e916900c0 · outbound

This paper cites gsplat: An open-source library for gaussian splatting.

GViT: Representing Images as Gaussians for Visual Recognition gsplat: An open-source library for gaussian splatting

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T21:47:13.546287Z

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Observation 3daf0d46-cb1e-448f-a58b-7f7da4af2f18 · outbound

This paper cites Vector-quantized Image Modeling with Improved VQGAN.

GViT: Representing Images as Gaussians for Visual Recognition Vector-quantized Image Modeling with Improved VQGAN

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T21:47:13.608125Z

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Observation ba8285a7-b6e8-454e-b4eb-2ab2b9d54e0a · outbound

This paper cites A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond.

GViT: Representing Images as Gaussians for Visual Recognition A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T21:47:13.697596Z

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Observation 8da1c4bd-7218-4bd1-8d90-9ac99f21af3d · outbound

This paper cites Gaussianimage: 1000 fps image representation and compression by 2d gaussian splatting.

GViT: Representing Images as Gaussians for Visual Recognition Gaussianimage: 1000 fps image representation and compression by 2d gaussian splatting

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:14.800213Z

Source-reported events for the cited work

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Observation 9d935b03-d3ab-47a7-9c6d-74efc9df2911 · outbound

This paper cites Self-supervised learning of object parts for semantic segmentation.

GViT: Representing Images as Gaussians for Visual Recognition Self-supervised learning of object parts for semantic segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:47:14.591969Z

Source-reported events for the cited work

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