Paper Citation Record · LEDGER

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis

As of 20 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 4 inbound Pith citation observations for arXiv:2501.09333.

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

pith.paper-citation-record.v1
2501.09333 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:10:16.073663Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:33:24.363074Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:09:15.243898Z

Reference resolution

54 of 54 outbound references displayed

verified exact1
verified fuzzy35
unresolved18
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2268e2c9-c735-4f74-9afa-17992bc685a3 · outbound

This paper cites Quantifying Attention Flow in Transformers.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Quantifying Attention Flow in Transformers

Reference 1

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Observation 2ca86b9f-5188-45a8-abb9-175f2034c96b · outbound

This paper cites Food-101–mining discriminative components with random forests.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Food-101–mining discriminative components with random forests

Reference 2

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Observation f5236ac9-f886-4587-9da3-09a37b7acd31 · outbound

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Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Unresolved cited work

Reference 3

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Observation 3fbb4bb8-4a25-4fb4-86a6-3588893610a6 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Emerg- ing properties in self-supervised vision transformers

Reference 4

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Observation 3557f26f-479c-4a2a-bf7e-189d8eb9284e · outbound

This paper cites Transformer inter- pretability beyond attention visualization.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Transformer inter- pretability beyond attention visualization

Reference 5

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Observation 5f8b911f-b79e-4efb-9129-8baa87aa0872 · outbound

This paper cites This looks like that: deep learn- ing for interpretable image recognition.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis This looks like that: deep learn- ing for interpretable image recognition

Reference 6

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Observation 399e65e6-9365-48f2-96e3-681a34723dec · outbound

This paper cites Vision transformers need registers.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Vision transformers need registers

Reference 7

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Observation 24b80250-c229-4a6a-b793-d00083e5a457 · outbound

This paper cites Robust learning with progressive data expansion against spurious correlation.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Robust learning with progressive data expansion against spurious correlation

Reference 8

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Observation c35317f9-595d-4168-8676-bbf10563d4a0 · outbound

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

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

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Observation 9defa35b-2a0e-41aa-ab18-5efe3afe01c5 · outbound

This paper cites Transfg: A trans- former architecture for fine-grained recognition.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Transfg: A trans- former architecture for fine-grained recognition

Reference 10

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Observation 73c4393e-d591-4301-bf48-dcb875ed3b7f · outbound

This paper cites The spectre of ‘spurious’ correlations.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis The spectre of ‘spurious’ correlations

Reference 11

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Observation 18db51fe-fa9d-4e42-b4f9-5107a54c9ce2 · outbound

This paper cites Vi- sual prompt tuning.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Vi- sual prompt tuning

Reference 12

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Observation ce0b415f-10c6-4c94-8943-f8818abcd0a6 · outbound

This paper cites Layercam: Exploring hierarchical class activation maps for localization.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Layercam: Exploring hierarchical class activation maps for localization

Reference 13

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Observation 89af43d9-aaff-4db8-b708-25ca1bad80cc · outbound

This paper cites Explainability of Vision Transformers: A Comprehensive Review and New Perspectives.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Explainability of Vision Transformers: A Comprehensive Review and New Perspectives

Reference 14

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Observation 970e2297-cb85-4f5e-9ded-3ee97f142c53 · outbound

This paper cites Novel dataset for fine-grained image categorization: Stanford dogs.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Novel dataset for fine-grained image categorization: Stanford dogs

Reference 15

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Observation 38e83efd-8b7b-446c-a85d-c42c12357e00 · outbound

This paper cites 3d object representations for fine-grained categorization.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis 3d object representations for fine-grained categorization

Reference 16

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Observation 29500658-adba-4498-8726-0e199f10bf8d · outbound

This paper cites Transcam: Transformer attention-based cam refinement for weakly supervised semantic segmentation.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Transcam: Transformer attention-based cam refinement for weakly supervised semantic segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:16.547906Z

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Observation af972742-7029-435c-8cc7-bf5444b73348 · outbound

This paper cites Re- moteclip: A vision language foundation model for remote sensing.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Re- moteclip: A vision language foundation model for remote sensing

Reference 18

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Observation 6501b9e0-4ae2-462a-8973-29c4e513f7fe · outbound

This paper cites Visual instruction tuning.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Visual instruction tuning

Reference 19

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Observation 17d475df-23e9-4b69-9ae1-0210cb196eee · outbound

This paper cites Query2Label: A Simple Transformer Way to Multi-Label Classification.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Query2Label: A Simple Transformer Way to Multi-Label Classification

Reference 20

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Observation 26a80dcd-f9a1-47d5-96de-2aa13218302e · outbound

This paper cites This looks like those: Illuminating prototypical con- cepts using multiple visualizations.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis This looks like those: Illuminating prototypical con- cepts using multiple visualizations

Reference 21

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Observation dd887520-4dd7-488f-aef3-36cf17788793 · outbound

This paper cites Fine-tuning is fine, if cal- ibrated.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Fine-tuning is fine, if cal- ibrated

Reference 22

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Observation fcee0785-9ef6-47d0-b506-b3d0bd789cfc · outbound

This paper cites Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition

Reference 23

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Observation 5c7de643-d897-4068-8324-21be3606eb80 · outbound

This paper cites Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images

Reference 24

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Observation 2f7042d9-8eee-4169-8bb1-db8fcda74603 · outbound

This paper cites Eigen-cam: Class activation map using principal compo- nents.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Eigen-cam: Class activation map using principal compo- nents

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:16.493203Z

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Observation 010eb740-5a50-403c-8880-1fee1ed65b96 · outbound

This paper cites Neural prototype trees for interpretable fine-grained image recogni- tion.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Neural prototype trees for interpretable fine-grained image recogni- tion

Reference 26

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Observation e0306879-2720-4da7-ae47-247b2a81c299 · outbound

This paper cites DreamCreature: Crafting Photorealistic Virtual Creatures from Imagination.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis DreamCreature: Crafting Photorealistic Virtual Creatures from Imagination

Reference 27

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Observation 60f856a2-70d7-4c36-bd30-520993c395d7 · outbound

This paper cites Automated flower classification over a large number of classes.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Automated flower classification over a large number of classes

Reference 28

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Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Unresolved cited work

Reference 29

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Observation 1ed44771-402f-4e7c-9803-0d53bcbbad77 · outbound

This paper cites Cats and dogs.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Cats and dogs

Reference 30

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Observation a9f82811-1340-4b8e-b0fe-109dceab0527 · outbound

This paper cites A simple interpretable transformer for fine-grained image classifica- tion and analysis.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis A simple interpretable transformer for fine-grained image classifica- tion and analysis

Reference 31

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Observation 499ac09b-ba9e-4fb0-84bd-c3fcb4510810 · outbound

This paper cites RISE: Randomized Input Sampling for Explanation of Black-box Models.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis RISE: Randomized Input Sampling for Explanation of Black-box Models

Reference 32

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This paper cites Birds 525 species - image classification.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Birds 525 species - image classification

Reference 33

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This paper cites Attention-based interpretabil- ity with concept transformers.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Attention-based interpretabil- ity with concept transformers

Reference 34

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

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis High-resolution image synthesis with latent diffusion models

Reference 35

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Observation 5c336002-e323-4816-94c7-94d7a83f1f79 · outbound

This paper cites Medicinal Leaf Dataset, 2020.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Medicinal Leaf Dataset, 2020

Reference 36

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Observation 928cb033-00f7-40bb-8ac8-3a7a893d1733 · outbound

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

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 37

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This paper cites Bioclip: A vision foundation model for the tree of life.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Bioclip: A vision foundation model for the tree of life

Reference 38

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This paper cites Emergent correspondence from image diffusion.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Emergent correspondence from image diffusion

Reference 39

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This paper cites Weakly supervised posture mining for fine-grained classi- fication.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Weakly supervised posture mining for fine-grained classi- fication

Reference 40

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This paper cites Rare Species Dataset, 2023.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Rare Species Dataset, 2023

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:16.361915Z

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Observation ee9cae14-9076-4674-b68b-65d742a458bc · outbound

This paper cites Visual query tuning: Towards effective usage of intermediate representa- tions for parameter and memory efficient transfer learning.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Visual query tuning: Towards effective usage of intermediate representa- tions for parameter and memory efficient transfer learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:16.351670Z

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Observation c082f65b-4a07-42e5-8a04-8faba44d19a6 · outbound

This paper cites Benchmarking rep- resentation learning for natural world image collections.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Benchmarking rep- resentation learning for natural world image collections

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:16.341695Z

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Observation 6bd0a666-b008-4529-ac7d-509b205d3e49 · outbound

This paper cites Gomez, Łukasz Kaiser, and Illia Polosukhin.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:16.330889Z

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Observation 44303bf9-0e90-43e7-bbe3-df94410b8ca4 · outbound

This paper cites The caltech-ucsd birds-200-2011 dataset.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis The caltech-ucsd birds-200-2011 dataset

Reference 45

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Observation 45b81d56-26cc-4454-a1b8-18de9c25e403 · outbound

This paper cites Score-cam: Score-weighted visual explanations for convolutional neural networks.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Score-cam: Score-weighted visual explanations for convolutional neural networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:16.313526Z

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Observation 5c8f6bb7-d5ef-44c1-957b-99ce7ad5defe · outbound

This paper cites Interpretable image recognition by constructing transparent embedding space.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Interpretable image recognition by constructing transparent embedding space

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:16.302754Z

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Observation 996b3568-3d06-405a-83fa-639e8bc4048c · outbound

This paper cites Open-set fine-grained retrieval via prompting vision-language evaluator.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Open-set fine-grained retrieval via prompting vision-language evaluator

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:16.291080Z

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Observation 8c0e7262-4893-46fa-b3b5-9249d9f781d5 · outbound

This paper cites Ip102: A large-scale benchmark dataset for insect pest recognition.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Ip102: A large-scale benchmark dataset for insect pest recognition

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:16.279382Z

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Observation b21e6fbf-d9d4-490f-a884-911875b45852 · outbound

This paper cites Multi-class token transformer for weakly supervised semantic segmentation.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Multi-class token transformer for weakly supervised semantic segmentation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:16.267511Z

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Observation 7a56b345-0a10-4d28-8011-d4fb9552e896 · outbound

This paper cites Protopformer: Concentrating on prototypical parts in vision transform- ers for interpretable image recognition.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Protopformer: Concentrating on prototypical parts in vision transform- ers for interpretable image recognition

Reference 51

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This paper cites Learning deep features for discrimina- tive localization.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Learning deep features for discrimina- tive localization

Reference 52

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Observation 998fd4f1-8d1c-4f32-ad38-870c649a6313 · outbound

This paper cites Learning to prompt for vision-language models.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis Learning to prompt for vision-language models

Reference 53

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This paper cites learnable prototypes.

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis learnable prototypes

Reference 54

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Pith citing papers

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AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models cites this paper.

AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis

Reference 14

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Uncovering Conceptual Blindspots in Generative Image Models Using Sparse Autoencoders cites this paper.

Uncovering Conceptual Blindspots in Generative Image Models Using Sparse Autoencoders Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis

Reference 57

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FOCUS: Fused Observation of Channels for Unveiling Spectra cites this paper.

FOCUS: Fused Observation of Channels for Unveiling Spectra Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis

Reference 8

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Observation ec2429a4-bf09-4f52-b0e8-aedb18196f58 · inbound

LARE: Low-Attention Region Encoding for Text-Image Retrieval cites this paper.

LARE: Low-Attention Region Encoding for Text-Image Retrieval Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis

Reference 60

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