Paper Citation Record · LEDGER

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification

As of 8 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2603.17390.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2603.17390 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T23:13:52.470831Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T11:56:40.836234Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T11:59:59.526090Z

Reference resolution

47 of 47 outbound references displayed

verified exact0
verified fuzzy0
unresolved47
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6fb650e8-f44e-4eec-8115-7019dfc7ed4f · outbound

This paper cites GPT-4 Technical Report.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

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Observation 029a0296-8fcb-4eeb-8abf-b300cd29c447 · outbound

This paper cites Opensurfaces: A richly annotated catalog of surface appear- ance.ACM TOG, 32(4):1–17, 2013.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Opensurfaces: A richly annotated catalog of surface appear- ance.ACM TOG, 32(4):1–17, 2013

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

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Observation b360a319-520e-4e26-b0cc-7e9c8ba35864 · outbound

This paper cites Material recognition in the wild with the materials in context database.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Material recognition in the wild with the materials in context database

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

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Observation 8b1af336-e35a-4d44-a16b-9b823fe89c13 · outbound

This paper cites Rgb road scene material segmentation.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Rgb road scene material segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

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Observation 4c2f9c77-086d-4a0b-b7f3-dc49290d378c · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54a538b3-91fc-48ac-acec-af44e2f7501a · outbound

This paper cites Zest: Zero-shot material trans- fer from a single image.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Zest: Zero-shot material trans- fer from a single image

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1495b0b-1473-41ad-b13b-b2725a96a77f · outbound

This paper cites Deep filter banks for texture recognition and segmentation.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Deep filter banks for texture recognition and segmentation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

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Observation 3505dfd4-d3f7-48ba-ba6c-08e139e93d20 · outbound

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

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d4debc8-b9b8-4498-a10d-38e36c8c3838 · outbound

This paper cites One-shot recognition of any material anywhere using contrastive learning with physics-based ren- dering.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification One-shot recognition of any material anywhere using contrastive learning with physics-based ren- dering

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56b1e9d8-8754-4617-b42a-c8edfb437a6b · outbound

This paper cites Diversify your vision datasets with automatic diffusion-based augmentation.Ad- vances in neural information processing systems, 36:79024– 79034, 2023.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Diversify your vision datasets with automatic diffusion-based augmentation.Ad- vances in neural information processing systems, 36:79024– 79034, 2023

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afcc0d31-2b96-425c-8733-77f33c783122 · outbound

This paper cites Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32f687f1-a8a0-4d38-82ce-67f0f15e317c · outbound

This paper cites MatFormer: A Generative Model for Procedural Materials.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification MatFormer: A Generative Model for Procedural Materials

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea3cb777-ff0e-4444-9582-fda29be88731 · outbound

This paper cites MaterialGAN: Reflectance Capture using a Generative SVBRDF Model.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification MaterialGAN: Reflectance Capture using a Generative SVBRDF Model

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

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Observation 24f7b20d-ce65-459c-9eee-0822431ed8dd · outbound

This paper cites Deep residual learning for image recognition.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Deep residual learning for image recognition

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

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Observation 34245908-722a-48aa-84c5-59723dd8e5e4 · outbound

This paper cites Masked autoencoders are scalable vision learners.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Masked autoencoders are scalable vision learners

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5466e6da-6a67-4be5-8bdc-d201fe6ab2cf · outbound

This paper cites Controlling material appearance by examples.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Controlling material appearance by examples

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afe4837a-e1d7-4e91-a4e8-81baaeb901cc · outbound

This paper cites Generating procedural materials from text or image prompts.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Generating procedural materials from text or image prompts

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f50bac02-616d-4736-9de1-9cffe617c2ef · outbound

This paper cites Material Anything: Generating Materials for Any 3D Object via Diffusion.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Material Anything: Generating Materials for Any 3D Object via Diffusion

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

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Unavailable: canonical work link unavailable.

Observation b5128915-c0a0-477a-9a41-faa45b416bf2 · outbound

This paper cites Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe536b36-63c6-4d47-a0f1-0d73833da200 · outbound

This paper cites Materialseg3d: Segmenting dense materi- als from 2d priors for 3d assets.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Materialseg3d: Segmenting dense materi- als from 2d priors for 3d assets

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5fe2aed-eebb-4802-8779-1139acb64e4b · outbound

This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f428870d-6a58-40a6-b602-eb5aaedac320 · outbound

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

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Swin transformer: Hierarchical vision transformer using shifted windows

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e86613c-b50e-4120-af80-b02295c668a8 · outbound

This paper cites Material palette: Extraction of materials from a single image.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Material palette: Extraction of materials from a single image

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29a3ef9a-e17c-43c5-ac20-e39bb8a65a6d · outbound

This paper cites Glass segmentation using intensity and spectral polarization cues.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Glass segmentation using intensity and spectral polarization cues

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97718bc1-2c6b-4307-952c-5caabd410072 · outbound

This paper cites A dataset of multi-illumination images in the wild.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification A dataset of multi-illumination images in the wild

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7fd8125-3d94-484f-a2cc-3b2072f4ef06 · outbound

This paper cites Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation.Advances in Neural Information Processing Systems, 36, 2024.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation.Advances in Neural Information Processing Systems, 36, 2024

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9bf6646-57e0-4ae3-949d-ea8072015024 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification DINOv2: Learning Robust Visual Features without Supervision

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

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Observation c6733bd8-e970-4473-8d34-c0ea3de05dee · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3600d7d2-b83d-4769-b470-0cc4fad886ca · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Learning transferable visual models from natural language supervi- sion

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

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Observation ea21fc4f-5283-4f3a-9e79-d3a9d214f788 · outbound

This paper cites Zero-shot text-to-image generation.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Zero-shot text-to-image generation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29dff506-069f-4190-b0ab-27fbe5c0e6f0 · outbound

This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5f9f4f5-8d2e-48c1-a97b-aa2b691b9916 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification High-resolution image synthesis with latent diffusion models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

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Observation 59918d37-53e6-45b6-85aa-be7ea61d9cf8 · outbound

This paper cites an unresolved cited work.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Unresolved cited work

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23ae4835-7142-40de-a56d-3d0886958fd4 · outbound

This paper cites Alchemist: Parametric control of material proper- ties with diffusion models.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Alchemist: Parametric control of material proper- ties with diffusion models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

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Observation 680070b1-8f03-4f48-a673-a62cebf10d5d · outbound

This paper cites High-Resolution Representations for Labeling Pixels and Regions.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification High-Resolution Representations for Labeling Pixels and Regions

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b94c0517-e192-44e2-a3f8-61d6102c9dd4 · outbound

This paper cites Satsynth: Augmenting image-mask pairs through diffusion models for aerial semantic segmentation.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Satsynth: Augmenting image-mask pairs through diffusion models for aerial semantic segmentation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdf05121-ef86-4410-94b6-86502ebb858b · outbound

This paper cites A dense material segmenta- tion dataset for indoor and outdoor scene parsing.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification A dense material segmenta- tion dataset for indoor and outdoor scene parsing

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

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Observation 232ae8c9-d10a-470b-844f-67a383516c75 · outbound

This paper cites A 4d light-field dataset and cnn architectures for material recogni- tion.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification A 4d light-field dataset and cnn architectures for material recogni- tion

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

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Observation 4db60ddc-dc37-4506-a747-946a6874559e · outbound

This paper cites Con- vnext v2: Co-designing and scaling convnets with masked autoencoders.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Con- vnext v2: Co-designing and scaling convnets with masked autoencoders

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06866fd7-e1d2-434d-ae55-e5709cddffd6 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Depth anything: Unleashing the power of large-scale unlabeled data

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f9fe2cb-8597-492e-a852-b6a601d7067f · outbound

This paper cites Mapa: Text-driven photorealistic mate- rial painting for 3d shapes.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Mapa: Text-driven photorealistic mate- rial painting for 3d shapes

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d2d5ab1-dc2c-4043-93aa-19a3085a442e · outbound

This paper cites Ti- legen: Tileable, controllable material generation and cap- ture.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Ti- legen: Tileable, controllable material generation and cap- ture

Reference 42

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1ba37b7-737e-4011-87d1-63434cf82f20 · outbound

This paper cites Photomat: A material generator learned from single flash photos.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Photomat: A material generator learned from single flash photos

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9814778-9cb6-4c6e-bb86-18f53777de9f · outbound

This paper cites reports the per-class classification accuracy on the DMS-test dataset.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification reports the per-class classification accuracy on the DMS-test dataset

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efc0061f-7f29-4ec5-bac6-2ae2047dbe9d · outbound

This paper cites Table 7 presents the class-wise classification accuracy on the Google-test dataset, comple- menting the averaged results in the main text.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Table 7 presents the class-wise classification accuracy on the Google-test dataset, comple- menting the averaged results in the main text

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a5f01dc-d581-41ba-88ae-3b5550269b52 · outbound

This paper cites While DMS exhibits significant imbal- ance across classes, our generative dataset provides a more uniform distribution, enabling better supervision across rare categories.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification While DMS exhibits significant imbal- ance across classes, our generative dataset provides a more uniform distribution, enabling better supervision across rare categories

Reference 46

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03bff204-ffbf-4dbd-ab80-2ff3d350d632 · outbound

This paper cites an unresolved cited work.

FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification Unresolved cited work

Reference 47

Resolution: unresolved; no resolver link, observed 2026-07-13T23:13:52.470831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation f6036e6b-710a-4f5e-bed1-cd27876168f6 · inbound

Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving cites this paper.

Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving FMMC: Harnessing the Power of Foundation Models for Accurate Material Classification

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-09T02:19:51.961048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.