GeoMM: On Geodesic Perspective for Multi-modal Learning | Paper Citation Record

Observation 66cfa46f-a0bd-4a78-a9c2-e6ee31dbede3 · outbound

This paper cites Geometry of oblique projections.

GeoMM: On Geodesic Perspective for Multi-modal Learning Geometry of oblique projections

Reference 1

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Observation 8659d41e-7dc6-4ffd-93b0-1cb8e5a0b684 · outbound

This paper cites Vqa: Visual question an- swering.

GeoMM: On Geodesic Perspective for Multi-modal Learning Vqa: Visual question an- swering

Reference 2

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This paper cites Geodesic matting: A framework for fast interactive image and video seg- mentation and matting.

GeoMM: On Geodesic Perspective for Multi-modal Learning Geodesic matting: A framework for fast interactive image and video seg- mentation and matting

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This paper cites Vlmo: Uni- fied vision-language pre-training with mixture-of- modality-experts.

GeoMM: On Geodesic Perspective for Multi-modal Learning Vlmo: Uni- fied vision-language pre-training with mixture-of- modality-experts

Reference 4

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This paper cites Grit-vlp: Grouped mini-batch sam- pling for efficient vision and language pre-training.

GeoMM: On Geodesic Perspective for Multi-modal Learning Grit-vlp: Grouped mini-batch sam- pling for efficient vision and language pre-training

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This paper cites Mafa: Managing false negatives for vision-language pre-training.

GeoMM: On Geodesic Perspective for Multi-modal Learning Mafa: Managing false negatives for vision-language pre-training

Reference 6

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This paper cites End-to-end object detection with trans- formers.

GeoMM: On Geodesic Perspective for Multi-modal Learning End-to-end object detection with trans- formers

Reference 7

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This paper cites Un- supervised learning of visual features by contrasting cluster assignments.

GeoMM: On Geodesic Perspective for Multi-modal Learning Un- supervised learning of visual features by contrasting cluster assignments

Reference 8

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Observation fb5ad8ad-31d2-4fa2-a7ab-c9bb930b2de1 · outbound

This paper cites Emerging properties in self-supervised vi- sion transformers.

GeoMM: On Geodesic Perspective for Multi-modal Learning Emerging properties in self-supervised vi- sion transformers

Reference 9

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Observation 99d13011-6d78-4073-b0c4-48eab8b4d2d9 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

GeoMM: On Geodesic Perspective for Multi-modal Learning Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 10

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Observation 274db597-f706-466c-94af-bff1aab1ab82 · outbound

This paper cites STAIR: Learning Sparse Text and Image Representation in Grounded Tokens.

GeoMM: On Geodesic Perspective for Multi-modal Learning STAIR: Learning Sparse Text and Image Representation in Grounded Tokens

Reference 11

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This paper cites Vlp: A survey on vision-language pre-training.

GeoMM: On Geodesic Perspective for Multi-modal Learning Vlp: A survey on vision-language pre-training

Reference 12

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Observation 2fe0d320-69d6-4761-914f-af8965a929be · outbound

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

GeoMM: On Geodesic Perspective for Multi-modal Learning A simple framework for con- trastive learning of visual representations

Reference 13

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This paper cites Exploring simple siamese representation learning.

GeoMM: On Geodesic Perspective for Multi-modal Learning Exploring simple siamese representation learning

Reference 14

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This paper cites Improved Baselines with Momentum Contrastive Learning.

GeoMM: On Geodesic Perspective for Multi-modal Learning Improved Baselines with Momentum Contrastive Learning

Reference 15

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This paper cites X-volution: On the unification of convolution and self-attention.

GeoMM: On Geodesic Perspective for Multi-modal Learning X-volution: On the unification of convolution and self-attention

Reference 16

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This paper cites Uniter: Universal image-text represen- tation learning.

GeoMM: On Geodesic Perspective for Multi-modal Learning Uniter: Universal image-text represen- tation learning

Reference 17

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Observation b5b4d7b4-0948-4db1-bff5-bb6998010a34 · outbound

This paper cites Unsupervised Opinion Summarization Using Approximate Geodesics.

GeoMM: On Geodesic Perspective for Multi-modal Learning Unsupervised Opinion Summarization Using Approximate Geodesics

Reference 18

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Observation 54f20193-659f-4878-8e05-b9d15bf37185 · outbound

This paper cites Geodesics in heat: A new approach to computing distance based on heat flow.

GeoMM: On Geodesic Perspective for Multi-modal Learning Geodesics in heat: A new approach to computing distance based on heat flow

Reference 19

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This paper cites Imagenet: A large-scale hierar- chical image database.

GeoMM: On Geodesic Perspective for Multi-modal Learning Imagenet: A large-scale hierar- chical image database

Reference 20

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

GeoMM: On Geodesic Perspective for Multi-modal Learning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 21

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Observation 563e7ed6-9e23-49c7-8249-556a7a4e2d39 · outbound

This paper cites Similarity reasoning and filtration for image-text matching.

GeoMM: On Geodesic Perspective for Multi-modal Learning Similarity reasoning and filtration for image-text matching

Reference 22

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This paper cites A note on two problems in con- nexion with graphs.

GeoMM: On Geodesic Perspective for Multi-modal Learning A note on two problems in con- nexion with graphs

Reference 23

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

GeoMM: On Geodesic Perspective for Multi-modal Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 24

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Observation 31a91c94-7b3c-49b9-b09a-29f0d3911a19 · outbound

This paper cites Algorithm 97: shortest path.

GeoMM: On Geodesic Perspective for Multi-modal Learning Algorithm 97: shortest path

Reference 25

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This paper cites Large-scale adversar- ial training for vision-and-language representation learning.

GeoMM: On Geodesic Perspective for Multi-modal Learning Large-scale adversar- ial training for vision-and-language representation learning

Reference 26

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This paper cites Imagebind: One embedding space to bind them all.

GeoMM: On Geodesic Perspective for Multi-modal Learning Imagebind: One embedding space to bind them all

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This paper cites Momentum contrast for unsupervised visual representation learning.

GeoMM: On Geodesic Perspective for Multi-modal Learning Momentum contrast for unsupervised visual representation learning

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This paper cites Masked autoen- coders are scalable vision learners.

GeoMM: On Geodesic Perspective for Multi-modal Learning Masked autoen- coders are scalable vision learners

Reference 29

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Observation fc0aedf2-1406-4814-93fc-e27dac959f1f · outbound

This paper cites Geonet: Deep geodesic networks for point cloud analysis.

GeoMM: On Geodesic Perspective for Multi-modal Learning Geonet: Deep geodesic networks for point cloud analysis

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Observation 276fe121-e048-4455-a1f0-4be44f6352e2 · outbound

This paper cites Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers.

GeoMM: On Geodesic Perspective for Multi-modal Learning Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers

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Observation 8ab911b9-f86b-4331-9caf-a74d29e77a59 · outbound

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

GeoMM: On Geodesic Perspective for Multi-modal Learning Scaling up visual and vision-language representation learning with noisy text supervision

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This paper cites Vilt: Vision-and-language transformer without convolu- tion or region supervision.

GeoMM: On Geodesic Perspective for Multi-modal Learning Vilt: Vision-and-language transformer without convolu- tion or region supervision

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This paper cites Computing geodesic paths on manifolds.

GeoMM: On Geodesic Perspective for Multi-modal Learning Computing geodesic paths on manifolds

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Observation 27c0734b-23d0-43c1-9612-5e6207f46290 · outbound

This paper cites Visual genome: Connecting language and vision using crowdsourced dense image annota- tions.

GeoMM: On Geodesic Perspective for Multi-modal Learning Visual genome: Connecting language and vision using crowdsourced dense image annota- tions

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Observation 7e43bedb-4f44-468c-bcf5-b3c469f4d285 · outbound

This paper cites Numba: a llvm-based python JIT compiler.

GeoMM: On Geodesic Perspective for Multi-modal Learning Numba: a llvm-based python JIT compiler

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This paper cites Le, Vu Nguyen, Chen-Ping Yu, and Dimitris Samaras.

GeoMM: On Geodesic Perspective for Multi-modal Learning Le, Vu Nguyen, Chen-Ping Yu, and Dimitris Samaras

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Observation 46ea7ca2-464f-42cf-bda9-8f1d75c8caee · outbound

This paper cites Stacked cross attention for image- text matching.

GeoMM: On Geodesic Perspective for Multi-modal Learning Stacked cross attention for image- text matching

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This paper cites Multimodal Foundation Models: From Specialists to General-Purpose Assistants.

GeoMM: On Geodesic Perspective for Multi-modal Learning Multimodal Foundation Models: From Specialists to General-Purpose Assistants

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This paper cites Align before fuse: Vision and lan- guage representation learning with momentum dis- tillation.

GeoMM: On Geodesic Perspective for Multi-modal Learning Align before fuse: Vision and lan- guage representation learning with momentum dis- tillation

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Observation f3f7b385-b0a0-4dbb-88c2-1a9fd92507e0 · outbound

This paper cites Blip: Bootstrapping language-image pre- training for unified vision-language understanding and generation.

GeoMM: On Geodesic Perspective for Multi-modal Learning Blip: Bootstrapping language-image pre- training for unified vision-language understanding and generation

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Observation 365af228-59ce-46e0-ae1c-7e91497c43c6 · outbound

This paper cites HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training.

GeoMM: On Geodesic Perspective for Multi-modal Learning HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training

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This paper cites VisualBERT: A Simple and Performant Baseline for Vision and Language.

GeoMM: On Geodesic Perspective for Multi-modal Learning VisualBERT: A Simple and Performant Baseline for Vision and Language

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Observation 4d8b67ea-7b08-4c7a-89fb-c7d019169cfe · outbound

This paper cites Oscar: Object-semantics aligned pre-training for vision-language tasks.

GeoMM: On Geodesic Perspective for Multi-modal Learning Oscar: Object-semantics aligned pre-training for vision-language tasks

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Observation b18e0303-3dcd-4727-b2b8-c843df5c473e · outbound

This paper cites Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm.

GeoMM: On Geodesic Perspective for Multi-modal Learning Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm

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Observation c4cc866d-c672-4f5d-9e80-c0fa2e1bc243 · outbound

This paper cites Scaling language- image pre-training via masking.

GeoMM: On Geodesic Perspective for Multi-modal Learning Scaling language- image pre-training via masking

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Observation 9254e8d4-3858-4246-a97a-f3fa3252d5e7 · outbound

This paper cites Geodesic self- attention for 3d point clouds.

GeoMM: On Geodesic Perspective for Multi-modal Learning Geodesic self- attention for 3d point clouds

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This paper cites Microsoft coco: Common objects in context.

GeoMM: On Geodesic Perspective for Multi-modal Learning Microsoft coco: Common objects in context

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GeoMM: On Geodesic Perspective for Multi-modal Learning Unresolved cited work

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Observation 39a9358a-fce3-4c28-a839-3c9deeedb799 · outbound

This paper cites Adap- tive reconstruction network for weakly supervised re- ferring expression grounding.

GeoMM: On Geodesic Perspective for Multi-modal Learning Adap- tive reconstruction network for weakly supervised re- ferring expression grounding

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Observation 2351d9c4-6ef2-45fb-8709-8d500a8fbb70 · outbound

This paper cites Algorithm as 136: A k-means clustering algorithm.

GeoMM: On Geodesic Perspective for Multi-modal Learning Algorithm as 136: A k-means clustering algorithm

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Observation 4d014c8a-5557-4533-8c80-b64489b474eb · outbound

This paper cites Decoupled weight decay regularization.

GeoMM: On Geodesic Perspective for Multi-modal Learning Decoupled weight decay regularization

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Observation fe50d3e3-97e6-4ba1-989f-c0f4b8c4e66e · outbound

This paper cites Vilbert: Pretraining task-agnostic visiolinguistic rep- resentations for vision-and-language tasks.Advances in Neural Information Processing Systems, 32, 2019.

GeoMM: On Geodesic Perspective for Multi-modal Learning Vilbert: Pretraining task-agnostic visiolinguistic rep- resentations for vision-and-language tasks.Advances in Neural Information Processing Systems, 32, 2019

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Observation 5246da00-04c3-45f3-8e9a-2f135549850b · outbound

This paper cites Computing geodesics on triangular meshes.Comput- ers & Graphics, 29(5):667–675, 2005.

GeoMM: On Geodesic Perspective for Multi-modal Learning Computing geodesics on triangular meshes.Comput- ers & Graphics, 29(5):667–675, 2005

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:40.042868Z

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This paper cites Bron- stein, and Pierre Vandergheynst.

GeoMM: On Geodesic Perspective for Multi-modal Learning Bron- stein, and Pierre Vandergheynst

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Observation db0dae6f-1b92-491a-9eec-622b4be87742 · outbound

This paper cites Jensen’s inequality.

GeoMM: On Geodesic Perspective for Multi-modal Learning Jensen’s inequality

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Observation 53b6afc6-3b03-4001-99e5-aa01f5ddb3f2 · outbound

This paper cites Towards bridging sample complexity and model capacity.

GeoMM: On Geodesic Perspective for Multi-modal Learning Towards bridging sample complexity and model capacity

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.995610Z

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Observation 037bcfe0-7300-451f-8c0e-90acd50e9d26 · outbound

This paper cites Towards interpreting and utiliz- ing symmetry property in adversarial examples.

GeoMM: On Geodesic Perspective for Multi-modal Learning Towards interpreting and utiliz- ing symmetry property in adversarial examples

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.980284Z

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Observation eb72ad7e-e4f0-4cb7-ac55-d7d92f501aee · outbound

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GeoMM: On Geodesic Perspective for Multi-modal Learning Exploring and utilizing pattern imbal- ance

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.965557Z

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Observation fc249f86-bb9e-4169-b426-df1f88ff3b4b · outbound

This paper cites MSSIDD: A Benchmark for Multi-Sensor Denoising.

GeoMM: On Geodesic Perspective for Multi-modal Learning MSSIDD: A Benchmark for Multi-Sensor Denoising

Reference 61

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GeoMM: On Geodesic Perspective for Multi-modal Learning Object- oriented anchoring and modal alignment in multi- modal learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.950715Z

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Observation ab70750a-7f52-41d8-b35d-9c13d9cf1988 · outbound

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GeoMM: On Geodesic Perspective for Multi-modal Learning Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:01:39.936600Z

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Observation dfa2bc74-bcff-4279-9090-48d22c9a014a · outbound

This paper cites Analytic inequalities.

GeoMM: On Geodesic Perspective for Multi-modal Learning Analytic inequalities

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.921950Z

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Observation 779b1cb0-4418-4c9a-b5ee-c5bbc4d44e24 · outbound

This paper cites Slip: Self-supervision meets language- image pre-training.

GeoMM: On Geodesic Perspective for Multi-modal Learning Slip: Self-supervision meets language- image pre-training

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.907114Z

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Observation fa827551-edcc-4c9e-9912-3714a5411439 · outbound

This paper cites Geodesic-former: A geodesic-guided few-shot 3d point cloud instance segmenter.

GeoMM: On Geodesic Perspective for Multi-modal Learning Geodesic-former: A geodesic-guided few-shot 3d point cloud instance segmenter

Reference 66

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Observation 365b3a40-91df-420d-8e36-a9d33cf78743 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

GeoMM: On Geodesic Perspective for Multi-modal Learning Representation Learning with Contrastive Predictive Coding

Reference 67

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Observation 02cfc387-40af-4776-aaca-1ce14fa83ce9 · outbound

This paper cites Im2text: Describing images using 1 million cap- tioned photographs.

GeoMM: On Geodesic Perspective for Multi-modal Learning Im2text: Describing images using 1 million cap- tioned photographs

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.874796Z

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Observation c050648a-b3e4-47df-b98c-50d09c10e2f9 · outbound

This paper cites Automatic differentiation in pytorch.

GeoMM: On Geodesic Perspective for Multi-modal Learning Automatic differentiation in pytorch

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.860351Z

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Observation cdca1922-b4c5-44fe-b249-9b550c72b70f · outbound

This paper cites BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers.

GeoMM: On Geodesic Perspective for Multi-modal Learning BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers

Reference 70

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Observation 963c7e38-774e-441b-a3ab-26fa8a413f82 · outbound

This paper cites Computational optimal transport: With applications to data science.

GeoMM: On Geodesic Perspective for Multi-modal Learning Computational optimal transport: With applications to data science

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.845941Z

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Observation b1c8fd83-b920-4b38-bd53-203dd2f7e424 · outbound

This paper cites Combined scaling for zero-shot transfer learn- ing.

GeoMM: On Geodesic Perspective for Multi-modal Learning Combined scaling for zero-shot transfer learn- ing

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.831296Z

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Observation a45f9e51-ef3b-4c23-9477-b6dc223754f4 · outbound

This paper cites Flickr30k entities: Collecting region-to- phrase correspondences for richer image-to-sentence models.

GeoMM: On Geodesic Perspective for Multi-modal Learning Flickr30k entities: Collecting region-to- phrase correspondences for richer image-to-sentence models

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.816611Z

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Observation 3aa8b2b5-f6c8-432a-bfad-9389940629e9 · outbound

This paper cites Straightest geodesics on polyhedral surfaces.

GeoMM: On Geodesic Perspective for Multi-modal Learning Straightest geodesics on polyhedral surfaces

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.800960Z

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Observation 69d4cb8f-1a1c-4d7f-beaf-ea39ec9da25a · outbound

This paper cites Graphwalks: Efficient shape agnostic geodesic shortest path estimation.

GeoMM: On Geodesic Perspective for Multi-modal Learning Graphwalks: Efficient shape agnostic geodesic shortest path estimation

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.786494Z

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Observation 83ece59c-1713-4f08-9db8-d05488a96e43 · outbound

This paper cites ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data.

GeoMM: On Geodesic Perspective for Multi-modal Learning ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-15T21:01:38.760337Z

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Observation b91a6b2b-8976-4bc4-9df4-028ed954f459 · outbound

This paper cites Improving language under- standing by generative pre-training.

GeoMM: On Geodesic Perspective for Multi-modal Learning Improving language under- standing by generative pre-training

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.771893Z

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Observation cf238571-3c66-4790-a28d-b0db7bb9eb3c · outbound

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

GeoMM: On Geodesic Perspective for Multi-modal Learning Learning transferable visual models from natural language supervision

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.756264Z

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Observation 34b842d2-b60f-445a-a601-22e99fb888e9 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.

GeoMM: On Geodesic Perspective for Multi-modal Learning Faster r-cnn: Towards real-time object detection with region proposal networks

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.739955Z

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Observation 8da1e150-ae73-490d-a9c7-3157e38dbdf1 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

GeoMM: On Geodesic Perspective for Multi-modal Learning Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.724753Z

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Observation 71a53bd5-3456-4d4e-aa00-1ceb46daee9c · outbound

This paper cites Conceptual captions: A cleaned, hy- pernymed, image alt-text dataset for automatic image captioning.

GeoMM: On Geodesic Perspective for Multi-modal Learning Conceptual captions: A cleaned, hy- pernymed, image alt-text dataset for automatic image captioning

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.709325Z

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Observation ef6e53a7-c2d1-4362-8650-182f8a060ede · outbound

This paper cites VL-BERT: Pre-training of Generic Visual-Linguistic Representations.

GeoMM: On Geodesic Perspective for Multi-modal Learning VL-BERT: Pre-training of Generic Visual-Linguistic Representations

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-15T21:01:38.788840Z

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Observation 37bb562b-fa2e-443f-b0de-ce00af6ef2dd · outbound

This paper cites PandaGPT: One Model To Instruction-Follow Them All.

GeoMM: On Geodesic Perspective for Multi-modal Learning PandaGPT: One Model To Instruction-Follow Them All

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T21:01:38.793508Z

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Observation 2d304298-9594-4627-bfbf-0b7daf9418fe · outbound

This paper cites A Corpus for Reasoning About Natural Language Grounded in Photographs.

GeoMM: On Geodesic Perspective for Multi-modal Learning A Corpus for Reasoning About Natural Language Grounded in Photographs

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-15T21:01:38.798297Z

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Observation 140a7beb-1be5-4a53-ad80-278df7e9ca50 · outbound

This paper cites Revisiting unreasonable effective- ness of data in deep learning era.

GeoMM: On Geodesic Perspective for Multi-modal Learning Revisiting unreasonable effective- ness of data in deep learning era

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.694704Z

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Observation 80e6a150-979d-4e34-96b5-8e289fb4060b · outbound

This paper cites Gortler, and Hugues Hoppe.

GeoMM: On Geodesic Perspective for Multi-modal Learning Gortler, and Hugues Hoppe

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.680072Z

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Observation 71871314-e8da-4eb1-821a-1a4f8785f83e · outbound

This paper cites LXMERT: learning cross-modality encoder representations from trans- formers.

GeoMM: On Geodesic Perspective for Multi-modal Learning LXMERT: learning cross-modality encoder representations from trans- formers

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.664840Z

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Observation 06bf8c74-6ea4-4cce-afc8-ea3c2782f5ee · outbound

This paper cites Tenenbaum, Vin de Silva, and John C.

GeoMM: On Geodesic Perspective for Multi-modal Learning Tenenbaum, Vin de Silva, and John C

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.650272Z

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Observation 65913de9-b905-4a15-910a-51ccf44688da · outbound

This paper cites Pigeon hole principle.

GeoMM: On Geodesic Perspective for Multi-modal Learning Pigeon hole principle

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.635830Z

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Observation 403d64ff-a537-4552-8f49-4041e217dbac · outbound

This paper cites Attention is all you need.

GeoMM: On Geodesic Perspective for Multi-modal Learning Attention is all you need

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.621277Z

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Observation db607275-2142-4af8-8dd8-14cb766e6385 · outbound

This paper cites Optimal transport: old and new.

GeoMM: On Geodesic Perspective for Multi-modal Learning Optimal transport: old and new

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.606340Z

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Observation 2dcf3c28-e99f-4ec3-ae7c-e8c33c20b304 · outbound

This paper cites Learning to combine: Knowledge aggrega- tion for multi-source domain adaptation.

GeoMM: On Geodesic Perspective for Multi-modal Learning Learning to combine: Knowledge aggrega- tion for multi-source domain adaptation

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.591726Z

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Observation 7c0b0f61-e2b7-46d9-bb19-459480b1a9e1 · outbound

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

GeoMM: On Geodesic Perspective for Multi-modal Learning Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-15T21:01:38.840420Z

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Observation 55b012a0-b38b-49f9-b321-51c4f2c794b8 · outbound

This paper cites Mvp: Multimodality-guided visual pre-training.

GeoMM: On Geodesic Perspective for Multi-modal Learning Mvp: Multimodality-guided visual pre-training

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.577128Z

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Observation deaa2c2d-f7ac-4aec-a500-d1e3e843a593 · outbound

This paper cites Visual Entailment: A Novel Task for Fine-Grained Image Understanding.

GeoMM: On Geodesic Perspective for Multi-modal Learning Visual Entailment: A Novel Task for Fine-Grained Image Understanding

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-15T21:01:38.849934Z

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Observation 35dd7e54-89fe-4315-8fb4-62c1fc13119e · outbound

This paper cites A fast proximal point method for computing exact wasserstein distance.

GeoMM: On Geodesic Perspective for Multi-modal Learning A fast proximal point method for computing exact wasserstein distance

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.562192Z

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Observation 9f1ffb73-0c4f-45eb-8907-460c0cd33c99 · outbound

This paper cites Vision-language pre- training with triple contrastive learning.

GeoMM: On Geodesic Perspective for Multi-modal Learning Vision-language pre- training with triple contrastive learning

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.547744Z

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Observation 89478e07-0d26-4164-881d-72924a471f13 · outbound

This paper cites Unified contrastive learning in image-text-label space.

GeoMM: On Geodesic Perspective for Multi-modal Learning Unified contrastive learning in image-text-label space

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.533274Z

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Observation 957b594d-6f13-47fe-96d9-93b44a7a7eb9 · outbound

This paper cites FILIP: Fine-grained Interactive Language-Image Pre-Training.

GeoMM: On Geodesic Perspective for Multi-modal Learning FILIP: Fine-grained Interactive Language-Image Pre-Training

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-15T21:01:38.868708Z

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Observation 5c558f01-be97-4bf8-aa03-68cd271fc20d · outbound

This paper cites FILIP: fine-grained interactive language-image pre-training.

GeoMM: On Geodesic Perspective for Multi-modal Learning FILIP: fine-grained interactive language-image pre-training

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:01:39.518572Z

Source-reported events for the cited work

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Observation 132e1aec-03d9-42ba-a78b-d52284e19a45 · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

GeoMM: On Geodesic Perspective for Multi-modal Learning CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-15T21:01:38.878311Z

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