RADIO1D: Elastic Representations for Condensed Vision Modeling | Paper Citation Record

Observation 66caf685-be41-48be-a201-9ebaf35dc24a · outbound

This paper cites Learning transferable visual models from natural language supervision.

RADIO1D: Elastic Representations for Condensed Vision Modeling Learning transferable visual models from natural language supervision

Reference 1

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Observation 649677a9-321a-42cc-99eb-bb259d835013 · outbound

This paper cites BLIP-2: Bootstrapping language- image pre-training with frozen image encoders and large language models.

RADIO1D: Elastic Representations for Condensed Vision Modeling BLIP-2: Bootstrapping language- image pre-training with frozen image encoders and large language models

Reference 2

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Observation 83d05092-8465-40ae-a363-34acb78c5246 · outbound

This paper cites Language is not all you need: Aligning perception with language models.

RADIO1D: Elastic Representations for Condensed Vision Modeling Language is not all you need: Aligning perception with language models

Reference 3

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This paper cites Visual instruction tuning.

RADIO1D: Elastic Representations for Condensed Vision Modeling Visual instruction tuning

Reference 4

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This paper cites Sigmoid loss for language image pre-training.

RADIO1D: Elastic Representations for Condensed Vision Modeling Sigmoid loss for language image pre-training

Reference 5

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This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

RADIO1D: Elastic Representations for Condensed Vision Modeling SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 6

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This paper cites PaliGemma: A versatile 3B VLM for transfer.

RADIO1D: Elastic Representations for Condensed Vision Modeling PaliGemma: A versatile 3B VLM for transfer

Reference 7

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Observation 3425b0fc-02ef-4160-84f0-d0889b1b3e97 · outbound

This paper cites What matters when building vision-language models?.

RADIO1D: Elastic Representations for Condensed Vision Modeling What matters when building vision-language models?

Reference 8

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Observation 6f740f26-d7de-4d0d-aeb2-0c7793c251cd · outbound

This paper cites Cambrian-1: A fully open, vision-centric exploration of multimodal llms.

RADIO1D: Elastic Representations for Condensed Vision Modeling Cambrian-1: A fully open, vision-centric exploration of multimodal llms

Reference 9

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Observation 89826ebe-5819-4ffd-a570-9dbf1521d10b · outbound

This paper cites NVILA: Efficient Frontier Visual Language Models.

RADIO1D: Elastic Representations for Condensed Vision Modeling NVILA: Efficient Frontier Visual Language Models

Reference 10

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Observation 981597fb-7f4b-4ae1-89d9-0c02a279d687 · outbound

This paper cites Qwen3-VL Technical Report.

RADIO1D: Elastic Representations for Condensed Vision Modeling Qwen3-VL Technical Report

Reference 11

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This paper cites Radiov2.5: Improved baselines for agglomerative vision foun- dation models.

RADIO1D: Elastic Representations for Condensed Vision Modeling Radiov2.5: Improved baselines for agglomerative vision foun- dation models

Reference 12

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RADIO1D: Elastic Representations for Condensed Vision Modeling Unresolved cited work

Reference 13

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Observation 753be084-d9a9-4903-8007-a0d6e3da9b50 · outbound

This paper cites DINOv3.

RADIO1D: Elastic Representations for Condensed Vision Modeling DINOv3

Reference 14

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Observation c59013d7-ec72-4d46-9bfc-5f41d561a554 · outbound

This paper cites SAM 3: Segment Anything with Concepts.

RADIO1D: Elastic Representations for Condensed Vision Modeling SAM 3: Segment Anything with Concepts

Reference 15

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Observation 7486a3ee-c219-41a7-b09e-48ad9bd30107 · outbound

This paper cites Eagle: Exploring the design space for multimodal LLMs with mixture of encoders.

RADIO1D: Elastic Representations for Condensed Vision Modeling Eagle: Exploring the design space for multimodal LLMs with mixture of encoders

Reference 16

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This paper cites VILA-u: a unified foundation model integrating visual understanding and generation.

RADIO1D: Elastic Representations for Condensed Vision Modeling VILA-u: a unified foundation model integrating visual understanding and generation

Reference 17

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Observation d816267e-b8c2-4385-b7de-6804e3fa1cc6 · outbound

This paper cites Qwen-VL: A versatile vision-language model for under- standing, localization, text reading, and beyond,.

RADIO1D: Elastic Representations for Condensed Vision Modeling Qwen-VL: A versatile vision-language model for under- standing, localization, text reading, and beyond,

Reference 18

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RADIO1D: Elastic Representations for Condensed Vision Modeling Unresolved cited work

Reference 19

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Observation cdb03c63-ac96-41cd-b557-54e2890f3a6c · outbound

This paper cites LLaVA-UHD v2: an MLLM Integrating High-Resolution Semantic Pyramid via Hierarchical Window Transformer.

RADIO1D: Elastic Representations for Condensed Vision Modeling LLaVA-UHD v2: an MLLM Integrating High-Resolution Semantic Pyramid via Hierarchical Window Transformer

Reference 20

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Observation eec94014-4c85-4388-864c-9077dff19806 · outbound

This paper cites Scene parsing through ADE20K dataset.

RADIO1D: Elastic Representations for Condensed Vision Modeling Scene parsing through ADE20K dataset

Reference 21

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Observation 0b670147-5789-4d89-9d6b-49701cf1cbbd · outbound

This paper cites Schwing, Alexander Kirillov, and Rohit Girdhar.

RADIO1D: Elastic Representations for Condensed Vision Modeling Schwing, Alexander Kirillov, and Rohit Girdhar

Reference 22

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RADIO1D: Elastic Representations for Condensed Vision Modeling Unresolved cited work

Reference 23

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This paper cites DINOv3-driven se- mantic segmentation for landslide mapping in mountainous regions.Sensors, 26(2), 2026.

RADIO1D: Elastic Representations for Condensed Vision Modeling DINOv3-driven se- mantic segmentation for landslide mapping in mountainous regions.Sensors, 26(2), 2026

Reference 24

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Observation 7b208968-cf6b-45b3-80ee-1b1a2699b973 · outbound

This paper cites Similarity of neural network representations revisited.

RADIO1D: Elastic Representations for Condensed Vision Modeling Similarity of neural network representations revisited

Reference 25

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Reference 26

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This paper cites Lawrence Zit- nick, and Piotr Dollár.

RADIO1D: Elastic Representations for Condensed Vision Modeling Lawrence Zit- nick, and Piotr Dollár

Reference 27

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This paper cites C-radiov4 (tech report), 2026.

RADIO1D: Elastic Representations for Condensed Vision Modeling C-radiov4 (tech report), 2026

Reference 28

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This paper cites Pereira, and William Bialek.

RADIO1D: Elastic Representations for Condensed Vision Modeling Pereira, and William Bialek

Reference 29

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Observation 0b70eefb-7a76-4dc7-9032-7eb2101de28b · outbound

This paper cites Modeling by shortest data description.Automatica, 14(5):465–471, 1978.

RADIO1D: Elastic Representations for Condensed Vision Modeling Modeling by shortest data description.Automatica, 14(5):465–471, 1978

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This paper cites AM-RADIO: Agglomerative Vision Foundation Model Reduce All Domains Into One.

RADIO1D: Elastic Representations for Condensed Vision Modeling AM-RADIO: Agglomerative Vision Foundation Model Reduce All Domains Into One

Reference 31

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Observation 2e28156b-c771-4b87-8db6-2829b584e89f · outbound

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

RADIO1D: Elastic Representations for Condensed Vision Modeling An image is worth 16x16 words: Transformers for image recognition at scale

Reference 32

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This paper cites Flextok: Resam- pling images into 1d token sequences of flexible length.

RADIO1D: Elastic Representations for Condensed Vision Modeling Flextok: Resam- pling images into 1d token sequences of flexible length

Reference 33

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

RADIO1D: Elastic Representations for Condensed Vision Modeling Swin transformer: Hierarchical vision transformer using shifted windows

Reference 34

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This paper cites DataComp: In search of the next generation of multimodal datasets.

RADIO1D: Elastic Representations for Condensed Vision Modeling DataComp: In search of the next generation of multimodal datasets

Reference 35

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RADIO1D: Elastic Representations for Condensed Vision Modeling Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design

Reference 36

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This paper cites Cover and Joy A.

RADIO1D: Elastic Representations for Condensed Vision Modeling Cover and Joy A

Reference 37

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This paper cites NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model.

RADIO1D: Elastic Representations for Condensed Vision Modeling NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model

Reference 38

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This paper cites Towards vqa models that can read.

RADIO1D: Elastic Representations for Condensed Vision Modeling Towards vqa models that can read

Reference 39

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RADIO1D: Elastic Representations for Condensed Vision Modeling DocVQA: A Dataset for VQA on Document Images

Reference 40

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RADIO1D: Elastic Representations for Condensed Vision Modeling Unresolved cited work

Reference 41

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This paper cites OCRBench: On the hidden mys- tery of OCR in large multimodal models.Sci- ence China Information Sciences, 67(12):220102, dec 2024.

RADIO1D: Elastic Representations for Condensed Vision Modeling OCRBench: On the hidden mys- tery of OCR in large multimodal models.Sci- ence China Information Sciences, 67(12):220102, dec 2024

Reference 42

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RADIO1D: Elastic Representations for Condensed Vision Modeling OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning

Reference 43

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This paper cites A diagram is worth a dozen images.

RADIO1D: Elastic Representations for Condensed Vision Modeling A diagram is worth a dozen images

Reference 44

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This paper cites ChartQA: A benchmark for question answering about charts with visual and logical reasoning.

RADIO1D: Elastic Representations for Condensed Vision Modeling ChartQA: A benchmark for question answering about charts with visual and logical reasoning

Reference 45

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RADIO1D: Elastic Representations for Condensed Vision Modeling findings-acl.177

Reference 46

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This paper cites Mmmu: A massive multi-discipline multi- modal understanding and reasoning benchmark for expert agi.

RADIO1D: Elastic Representations for Condensed Vision Modeling Mmmu: A massive multi-discipline multi- modal understanding and reasoning benchmark for expert agi

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This paper cites Seed-bench: Benchmarking multimodal large language models.

RADIO1D: Elastic Representations for Condensed Vision Modeling Seed-bench: Benchmarking multimodal large language models

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This paper cites Longvideobench: A benchmark for long- context interleaved video-language understand- ing, 2024.

RADIO1D: Elastic Representations for Condensed Vision Modeling Longvideobench: A benchmark for long- context interleaved video-language understand- ing, 2024

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This paper cites Token merging: Your ViT but faster.

RADIO1D: Elastic Representations for Condensed Vision Modeling Token merging: Your ViT but faster

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This paper cites Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs.

RADIO1D: Elastic Representations for Condensed Vision Modeling Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs

Reference 51

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RADIO1D: Elastic Representations for Condensed Vision Modeling Generalized intersection over union

Reference 52

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RADIO1D: Elastic Representations for Condensed Vision Modeling nuScenes: A multimodal dataset for autonomous driving

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RADIO1D: Elastic Representations for Condensed Vision Modeling Vision transform- ers need registers

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RADIO1D: Elastic Representations for Condensed Vision Modeling An image is worth 32 tokens for reconstruction and generation

Reference 57

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This paper cites Net2Net: Accelerating Learning via Knowledge Transfer.

RADIO1D: Elastic Representations for Condensed Vision Modeling Net2Net: Accelerating Learning via Knowledge Transfer

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RADIO1D: Elastic Representations for Condensed Vision Modeling Unresolved cited work

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Observation a5a0d2a4-68a1-4e6e-a731-74058dca946f · outbound

This paper cites InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks.

RADIO1D: Elastic Representations for Condensed Vision Modeling InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

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Reference resolution

Outbound references

Pith citing papers