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Paper Citation Record · LEDGER
Multimodal Autoregressive Pre-training of Large Vision Encoders
As of 15 August 2026, this Paper Citation Record lists 100 of 137 outbound references and 20 inbound Pith citation observations for arXiv:2411.14402.
A citation records a reference. It does not transfer a finding from one paper to another.
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Reference resolution
100 of 137 outbound references displayed
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External citation measurements
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Outbound references
Observation 9b4ca4f7-aa21-4a23-b87f-385b1a76dfe2 · outbound
This paper cites GPT-4 Technical Report.
Multimodal Autoregressive Pre-training of Large Vision Encoders GPT-4 Technical Report
Reference 1
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Observation 5bc970ba-21f5-412f-9762-c173c791013b · outbound
This paper cites Nocaps: Novel object cap- tioning at scale.
Multimodal Autoregressive Pre-training of Large Vision Encoders Nocaps: Novel object cap- tioning at scale
Reference 2
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Observation 31e2e298-fb61-4d80-a9b0-73b813ff38a4 · outbound
This paper cites Flamingo: a visual language model for few-shot learning.
Multimodal Autoregressive Pre-training of Large Vision Encoders Flamingo: a visual language model for few-shot learning
Reference 3
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Observation 8b78b674-1330-43fe-af85-fe247ef76a06 · outbound
This paper cites Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture.
Multimodal Autoregressive Pre-training of Large Vision Encoders Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
Reference 4
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This paper cites Qwen Technical Report.
Multimodal Autoregressive Pre-training of Large Vision Encoders Qwen Technical Report
Reference 5
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Observation fdae0bd5-2d23-4638-8d52-81c17daf68d3 · outbound
This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.
Multimodal Autoregressive Pre-training of Large Vision Encoders Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
Reference 6
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Observation 6ca328a1-9710-41a6-a5d2-fec628540c1f · outbound
This paper cites From detection of individual metastases to classification of lymph node status at the pa- tient level: the camelyon17 challenge.
Multimodal Autoregressive Pre-training of Large Vision Encoders From detection of individual metastases to classification of lymph node status at the pa- tient level: the camelyon17 challenge
Reference 7
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Observation a0aaf7d9-e904-46f6-ac68-6ad90c6eac8e · outbound
This paper cites BEiT: Bert pre- training of image transformers.
Multimodal Autoregressive Pre-training of Large Vision Encoders BEiT: Bert pre- training of image transformers
Reference 8
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Observation bcf6dafb-1f3d-476c-ba2e-4b2f8ae9efe9 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Flexivit: One model for all patch sizes
Reference 9
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Multimodal Autoregressive Pre-training of Large Vision Encoders Food-101 – mining discriminative components with ran- dom forests
Reference 10
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Observation fea36b10-7818-4644-b7e3-81ca5fc6a9a1 · outbound
This paper cites Time series analysis: forecasting and control.
Multimodal Autoregressive Pre-training of Large Vision Encoders Time series analysis: forecasting and control
Reference 11
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Observation 1bfeb3e2-5170-4879-937f-83f4dd8060c3 · outbound
This paper cites Language Models are Few-Shot Learners.
Multimodal Autoregressive Pre-training of Large Vision Encoders Language Models are Few-Shot Learners
Reference 12
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Observation faf41b7a-c743-442e-83a2-6ecbf8723a24 · outbound
This paper cites Coyo-700m: Image-text pair dataset, 2022.
Multimodal Autoregressive Pre-training of Large Vision Encoders Coyo-700m: Image-text pair dataset, 2022
Reference 13
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Observation cfe5a846-2afb-449b-83c3-ba8d4b50bd97 · outbound
This paper cites End-to-end object detection with transformers.
Multimodal Autoregressive Pre-training of Large Vision Encoders End-to-end object detection with transformers
Reference 14
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Observation 9aae2335-5283-4c6f-9ae1-08d9149961fc · outbound
This paper cites Unsupervised learn- ing of visual features by contrasting cluster assignments.
Multimodal Autoregressive Pre-training of Large Vision Encoders Unsupervised learn- ing of visual features by contrasting cluster assignments
Reference 15
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This paper cites Emerging properties in self-supervised vision transformers.
Multimodal Autoregressive Pre-training of Large Vision Encoders Emerging properties in self-supervised vision transformers
Reference 16
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Observation bd86e4ca-80fa-4289-b35c-fe7d84ec4339 · outbound
This paper cites A generative approach for wikipedia-scale visual entity recognition.
Multimodal Autoregressive Pre-training of Large Vision Encoders A generative approach for wikipedia-scale visual entity recognition
Reference 17
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This paper cites Mmdetection: Open mmlab detection toolbox and benchmark.
Multimodal Autoregressive Pre-training of Large Vision Encoders Mmdetection: Open mmlab detection toolbox and benchmark
Reference 18
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This paper cites Generative pre- training from pixels.
Multimodal Autoregressive Pre-training of Large Vision Encoders Generative pre- training from pixels
Reference 19
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Observation 49f7072e-60e7-44f1-bc1e-2b55fb7d9bd3 · outbound
This paper cites A simple framework for contrastive learning of visual representations.
Multimodal Autoregressive Pre-training of Large Vision Encoders A simple framework for contrastive learning of visual representations
Reference 20
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Observation 98afe1f3-3b4f-4e56-a41f-3f6d42fbaaab · outbound
This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.
Multimodal Autoregressive Pre-training of Large Vision Encoders Microsoft COCO Captions: Data Collection and Evaluation Server
Reference 21
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Observation d2ad7346-77b2-48b3-a3da-8878fb5733a2 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Gonzalez, Ion Stoica, and Eric P
Reference 22
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Multimodal Autoregressive Pre-training of Large Vision Encoders PaLM: Scaling Language Modeling with Pathways
Reference 23
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Multimodal Autoregressive Pre-training of Large Vision Encoders Functional map of the world
Reference 24
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Multimodal Autoregressive Pre-training of Large Vision Encoders Cimpoi, S
Reference 25
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Multimodal Autoregressive Pre-training of Large Vision Encoders Patch n’pack: Navit, a vision trans- former for any aspect ratio and resolution
Reference 26
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Multimodal Autoregressive Pre-training of Large Vision Encoders Imagenet: A large-scale hierarchical im- age database
Reference 27
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Multimodal Autoregressive Pre-training of Large Vision Encoders Virtex: Learning visual representations from textual annotations
Reference 28
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Multimodal Autoregressive Pre-training of Large Vision Encoders Unsu- pervised visual representation learning by context predic- tion
Reference 29
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Multimodal Autoregressive Pre-training of Large Vision Encoders An image is worth 16x16 words: Trans- formers for image recognition at scale
Reference 30
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Multimodal Autoregressive Pre-training of Large Vision Encoders The Llama 3 Herd of Models
Reference 32
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Multimodal Autoregressive Pre-training of Large Vision Encoders Scalable Pre-training of Large Autoregressive Image Models
Reference 33
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Multimodal Autoregressive Pre-training of Large Vision Encoders Taming transformers for high-resolution image synthesis
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Multimodal Autoregressive Pre-training of Large Vision Encoders Data Filtering Networks
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Multimodal Autoregressive Pre-training of Large Vision Encoders Slowfast networks for video recognition
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Multimodal Autoregressive Pre-training of Large Vision Encoders Improved baselines for vision-language pre-training
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Multimodal Autoregressive Pre-training of Large Vision Encoders MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
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Multimodal Autoregressive Pre-training of Large Vision Encoders Unsupervised Representation Learning by Predicting Image Rotations
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Multimodal Autoregressive Pre-training of Large Vision Encoders Making the v in vqa matter: El- evating the role of image understanding in visual question answering
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Multimodal Autoregressive Pre-training of Large Vision Encoders Making the v in vqa matter: El- evating the role of image understanding in visual question answering
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Multimodal Autoregressive Pre-training of Large Vision Encoders Bootstrap your own latent-a new approach to self-supervised learning
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Multimodal Autoregressive Pre-training of Large Vision Encoders Lvis: A dataset for large vocabulary instance segmentation, 2019
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Multimodal Autoregressive Pre-training of Large Vision Encoders Vizwiz grand challenge: Answering visual questions from blind people
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Multimodal Autoregressive Pre-training of Large Vision Encoders Deep residual learning for image recognition
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Multimodal Autoregressive Pre-training of Large Vision Encoders Mask r-cnn
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Multimodal Autoregressive Pre-training of Large Vision Encoders Masked autoencoders are scal- able vision learners
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Multimodal Autoregressive Pre-training of Large Vision Encoders Eurosat: A novel dataset and deep learn- ing benchmark for land use and land cover classification,
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Multimodal Autoregressive Pre-training of Large Vision Encoders Scaling up vision-language pre-training for image captioning
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Multimodal Autoregressive Pre-training of Large Vision Encoders Gqa: A new dataset for real-world visual reasoning and compositional question answering
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Multimodal Autoregressive Pre-training of Large Vision Encoders Scaling up visual and vision-language representation learning with noisy text supervision
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Multimodal Autoregressive Pre-training of Large Vision Encoders Scaling Laws for Neural Language Models
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Multimodal Autoregressive Pre-training of Large Vision Encoders Referitgame: Referring to objects in pho- tographs of natural scenes
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Multimodal Autoregressive Pre-training of Large Vision Encoders Big transfer (bit): General visual representation learning
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Multimodal Autoregressive Pre-training of Large Vision Encoders 3d object representations for fine-grained categorization
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Multimodal Autoregressive Pre-training of Large Vision Encoders Learning multiple layers of features from tiny images
Reference 60
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Observation b9f089a3-89e1-425f-b757-0011b3f42c5b · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Imagenet classification with deep convolutional neural net- works
Reference 61
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Multimodal Autoregressive Pre-training of Large Vision Encoders MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
Reference 62
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Observation a833fbd6-1add-4ddf-8647-8e803f910412 · outbound
This paper cites Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models.
Multimodal Autoregressive Pre-training of Large Vision Encoders Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models
Reference 63
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Observation fa4c8a09-bab1-4142-96d4-0199b6d75596 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Reference 64
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Multimodal Autoregressive Pre-training of Large Vision Encoders Align before fuse: Vision and language representation learning with momentum distillation
Reference 65
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Observation 560a87bd-409a-4538-b435-258c73db3539 · outbound
This paper cites Blip: Bootstrapping language-image pre-training for uni- fied vision-language understanding and generation.
Multimodal Autoregressive Pre-training of Large Vision Encoders Blip: Bootstrapping language-image pre-training for uni- fied vision-language understanding and generation
Reference 66
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Observation 8198cf4a-a4ef-4afd-a553-3be1aabad19b · outbound
This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.
Multimodal Autoregressive Pre-training of Large Vision Encoders Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Reference 67
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Multimodal Autoregressive Pre-training of Large Vision Encoders Exploring plain vision transformer backbones for object de- tection
Reference 68
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Observation c7f349a3-6580-43ca-8b2e-a5e15848f4fd · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Exploring plain vision transformer backbones for object de- tection, 2022
Reference 69
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Multimodal Autoregressive Pre-training of Large Vision Encoders Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Reference 70
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Observation ac9ce47e-8e0b-4bd7-9f98-13c7930fda00 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Microsoft coco: Common objects in context
Reference 71
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Multimodal Autoregressive Pre-training of Large Vision Encoders SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models
Reference 72
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Observation f866b314-9cce-43a4-9011-b15f6c55691f · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Improved baselines with visual instruction tuning
Reference 73
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Observation d2ae77fa-0722-40cd-9765-4d6ad0daf1a8 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Grounding dino: Mar- rying dino with grounded pre-training for open-set object detection
Reference 74
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Observation 0047e8f2-658e-4f8f-b9e9-c3ddba6b01c7 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Sgdr: Stochastic gradient descent with warm restarts
Reference 75
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Observation a4fce8aa-c5aa-4817-be8f-0be5cd4befb5 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Decoupled Weight Decay Regularization
Reference 76
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Observation f1697871-86ca-4499-a580-a7acba5fca1f · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Unified-io 2: Scaling autoregressive mul- timodal models with vision language audio and action
Reference 77
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Observation 90824477-ae47-423c-bdf4-e46c9fa7e2ec · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Learn to explain: Multimodal reasoning via thought chains for science question answering
Reference 78
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Multimodal Autoregressive Pre-training of Large Vision Encoders Generation and comprehension of unambiguous object descriptions
Reference 79
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Observation 2ca744f4-295d-42da-b98d-afaa0a98c49f · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Ok-vqa: A visual question answering benchmark requiring external knowledge
Reference 80
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Observation 0abe84aa-6dee-4f3f-9be3-220987b200e0 · outbound
This paper cites Ok-vqa: A visual question answering benchmark requiring external knowledge.
Multimodal Autoregressive Pre-training of Large Vision Encoders Ok-vqa: A visual question answering benchmark requiring external knowledge
Reference 81
- Resolution
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Observation bea6496d-aa2e-4caf-b0d2-3b82603a3a1d · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
Reference 82
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Observation f8ecadcb-5000-4dbe-bf0a-f24340b5c8a3 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Docvqa: A dataset for vqa on document images
Reference 83
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Observation a853ea2a-9a0c-4984-8eac-8852141e4573 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Infographicvqa
Reference 84
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Observation 36a14cf9-c4b1-4240-aa1a-b7e7ca2a5d02 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Reference 85
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Observation 1d64046b-f57e-4bfb-ac4a-b991eab63b52 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Unsupervised learning of visual representations by solving jigsaw puzzles
Reference 86
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Multimodal Autoregressive Pre-training of Large Vision Encoders Unresolved cited work
Reference 87
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Observation 056ed4f9-b7c3-48f7-8054-b0d53f8977a4 · outbound
This paper cites an unresolved cited work.
Multimodal Autoregressive Pre-training of Large Vision Encoders Unresolved cited work
Reference 88
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Observation 981aae9a-5948-410d-9a05-2c1bcc9ca78f · outbound
This paper cites Moment matching for multi-source domain adaptation.
Multimodal Autoregressive Pre-training of Large Vision Encoders Moment matching for multi-source domain adaptation
Reference 89
- Resolution
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Observation 837b0f71-f317-4159-90cc-57ef100438dc · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Plummer, Liwei Wang, Christopher M
Reference 90
- Resolution
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Observation 70cf87f0-7534-407b-90ba-181f700b07fc · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum
Reference 91
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Observation f0fd77eb-00f8-4758-a39b-cb163db3bb9b · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Improving language understanding by genera- tive pre-training
Reference 92
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Observation a9206d5f-e916-4bcb-9e10-25881c872060 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Language models are unsupervised multitask learners
Reference 93
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Observation 73d0b2e4-c04c-459c-9fa9-1cc43ff0898a · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Learn- ing transferable visual models from natural language super- vision
Reference 94
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Observation 31576629-c6c8-42ba-b0e3-b70ab8335efc · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Exploring the limits of transfer learning with a unified text-to-text transformer
Reference 95
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Source-reported events for the cited work
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Observation 95e33792-7b00-4930-85a6-636fcca93cbf · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Reference 96
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Unavailable: canonical work link unavailable.
Observation f7bc0033-fea6-4e3e-b576-b92d6dd8c2c4 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders ImageNet-21K Pretraining for the Masses
Reference 97
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Observation a4e7862f-5c31-4469-9a18-e0f51c30bcef · outbound
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Reference 98
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Multimodal Autoregressive Pre-training of Large Vision Encoders Learning visual representations with caption annotations
Reference 99
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Source-reported events for the cited work
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Observation 6832edac-8faf-42c4-8946-8bedddc15daf · outbound
This paper cites Laion- 400m: Open dataset of clip-filtered 400 million image-text pairs.
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Reference 100
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Source-reported events for the cited work
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Observation a6202d2b-30c8-4fd7-b472-2a0244c38e96 · outbound
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Multimodal Autoregressive Pre-training of Large Vision Encoders Objects365: A large-scale, high-quality dataset for object detection
Reference 101
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Source-reported events for the cited work
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Pith citing papers
Observation b9a9bf37-d122-4f11-942b-87fddc10995f · inbound
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Reference 19
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Observation 35ae0f1d-efac-48c4-b0fa-6a96058e3f29 · inbound
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Reference 8
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Reference 9
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Reference 9
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Observation 8c3bff0d-e2ae-4cf5-a8d7-9c7ecdd809c9 · inbound
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Reference 20
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Source-reported events for the cited work
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Observation db3b232f-c938-47f5-8409-9c61c4a76f1f · inbound
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Reference 14
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Observation 1692588c-b51e-4fdc-ae1b-c2cdc1f82b46 · inbound
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Reference 2020
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Reference 50
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Observation 0728f44c-cc8d-4784-b3e7-b2147815bf8c · inbound
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Reference 17
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Observation 73860be0-67e3-4044-ad80-3203f726ffc8 · inbound
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Reference 20
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Observation 8f3a1ec8-383f-4a72-92bd-bacd9751eb5e · inbound
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Reference 21
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 0d1cdbad-61e0-4aa1-8757-c176ebea21e6 · inbound
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning cites this paper.
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning Multimodal Autoregressive Pre-training of Large Vision Encoders
Reference 22
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T00:33:50.796334Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 420672e1-1066-4026-9620-448ba0bb8c5f · inbound
SAILViT: Towards Robust and Generalizable Visual Backbones for MLLMs via Gradual Feature Refinement cites this paper.
SAILViT: Towards Robust and Generalizable Visual Backbones for MLLMs via Gradual Feature Refinement Multimodal Autoregressive Pre-training of Large Vision Encoders
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:53:04.264739Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a26c49c9-2976-4085-b180-0cb9d3af72cb · inbound
LaCo: Efficient Layer-wise Compression of Visual Tokens for Multimodal Large Language Models cites this paper.
LaCo: Efficient Layer-wise Compression of Visual Tokens for Multimodal Large Language Models Multimodal Autoregressive Pre-training of Large Vision Encoders
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:38:28.632831Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0318e99b-ce9f-42c5-bb61-293b850ed45c · inbound
OpenVision 2: A Family of Generative Pretrained Visual Encoders for Multimodal Learning cites this paper.
OpenVision 2: A Family of Generative Pretrained Visual Encoders for Multimodal Learning Multimodal Autoregressive Pre-training of Large Vision Encoders
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T12:22:33.329919Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 08145ee1-f2ae-4ef8-ac64-74f29d294ab4 · inbound
Hierarchical Pre-Training of Vision Encoders with Large Language Model cites this paper.
Hierarchical Pre-Training of Vision Encoders with Large Language Model Multimodal Autoregressive Pre-training of Large Vision Encoders
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T05:37:50.158868Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bb0a169b-0d0c-4acd-9423-c1c812e585c2 · inbound
Towards Generalizable Deepfake Image Detection with Vision Transformers cites this paper.
Towards Generalizable Deepfake Image Detection with Vision Transformers Multimodal Autoregressive Pre-training of Large Vision Encoders
Reference 6
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T06:41:36.897978Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 70d6a3f6-3ad7-4717-bdb5-3abddc3e4ec1 · inbound
SigLIP-HD by Fine-to-Coarse Supervision cites this paper.
SigLIP-HD by Fine-to-Coarse Supervision Multimodal Autoregressive Pre-training of Large Vision Encoders
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T02:39:58.984385Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 552d91c6-28e5-46c2-a490-ea569baa9126 · inbound
Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model cites this paper.
Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model Multimodal Autoregressive Pre-training of Large Vision Encoders
Reference 107
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T06:20:14.033388Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.