Paper Citation Record · LEDGER

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models

As of 23 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2505.07209.

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

pith.paper-citation-record.v1
2505.07209 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:26:48.737161Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:33:09.125847Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

52 of 52 outbound references displayed

verified exact0
verified fuzzy29
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eee37a5f-86ea-4266-af93-c16e674ec001 · outbound

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

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 1

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Observation 72a0fdbc-f715-4dd8-b769-0780701418e0 · outbound

This paper cites Interactive concept bottleneck models.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Interactive concept bottleneck models

Reference 2

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Observation 87ed5f87-e9e8-4668-a2e1-d8e49d043790 · outbound

This paper cites PLOT: Prompt Learning with Optimal Transport for Vision-Language Models.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

Reference 3

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Observation 7c6c56fa-c148-4213-bccb-20e21d7c8684 · outbound

This paper cites Graph optimal transport for cross-domain alignment.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Graph optimal transport for cross-domain alignment

Reference 4

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Observation 0381a0a6-471e-412b-aa07-693fdb119424 · outbound

This paper cites Detect what you can: Detecting and representing objects using holistic mod- els and body parts.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Detect what you can: Detecting and representing objects using holistic mod- els and body parts

Reference 5

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Observation 3d8e719f-9cf2-4479-a7a7-cd81142cdfcb · outbound

This paper cites Interpretable machine learning: A guide for making black box models explainable.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Interpretable machine learning: A guide for making black box models explainable

Reference 6

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Observation 534bf46f-1055-400f-91e8-e9a6d829ef59 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Sinkhorn distances: Lightspeed computation of optimal transport

Reference 7

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Observation d23cf8a1-a7af-4eb2-8c9f-3482519d01f3 · outbound

This paper cites Decision trees.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Decision trees

Reference 8

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Observation a595433b-0364-41a3-91fe-e2ed22e87c0d · outbound

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

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Imagenet: A large-scale hierarchical image database

Reference 9

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Observation 5c8c9299-b151-4e66-aeb6-7de4055027fd · outbound

This paper cites Concept embedding mod- els: Beyond the accuracy-explainability trade-off.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Concept embedding mod- els: Beyond the accuracy-explainability trade-off

Reference 10

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Observation 7cc5849c-dca3-4912-9e46-8c330fd480cc · outbound

This paper cites Learning to receive help: Intervention-aware concept embedding models.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Learning to receive help: Intervention-aware concept embedding models

Reference 11

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Observation c220ce56-da79-4cd9-9ba4-f6e6dd9505b3 · outbound

This paper cites Learning with a wasserstein loss.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Learning with a wasserstein loss

Reference 12

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Observation 4c97611a-70d2-45ad-a539-2b43d74e25ec · outbound

This paper cites Partimagenet: A large, high- quality dataset of parts.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Partimagenet: A large, high- quality dataset of parts

Reference 13

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Observation ed90eb38-ce15-4035-b72a-33c0909de506 · outbound

This paper cites An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models

Reference 14

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Observation 316a7b40-829c-416d-b048-475f246d5c35 · outbound

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

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Novel dataset for fine-grained image categorization

Reference 15

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Observation b97f774f-250b-4dc1-95e0-8e2bef64d568 · outbound

This paper cites Segment any- thing.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Segment any- thing

Reference 16

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Observation fc13761c-397f-4ee7-9e11-33aa50f0482f · outbound

This paper cites Concept bottleneck models.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Concept bottleneck models

Reference 17

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Observation e8d94d63-ea66-4e5e-a42a-c5ead4b56eef · outbound

This paper cites Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning

Reference 18

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Observation 158aa7aa-5f11-4baa-b521-977c7e25af89 · outbound

This paper cites Learning multiple layers of features from tiny images.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Learning multiple layers of features from tiny images

Reference 19

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Observation 912922b9-9156-48e7-b1c2-098c6134fbc9 · outbound

This paper cites Hierarchical optimal transport for multimodal distribution alignment.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Hierarchical optimal transport for multimodal distribution alignment

Reference 20

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Observation 2cc9d82e-3912-4707-bf74-b86373a5a186 · outbound

This paper cites Patchct: Align- ing patch set and label set with conditional transport for multi-label image classification.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Patchct: Align- ing patch set and label set with conditional transport for multi-label image classification

Reference 21

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Observation 08b1df82-b52a-45f0-8357-f3e44d7df1b5 · outbound

This paper cites Deal: Disentan- gle and localize concept-level explanations for vlms.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Deal: Disentan- gle and localize concept-level explanations for vlms

Reference 22

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Observation 6f0f4545-9a17-41e2-8fca-1f9c657f9787 · outbound

This paper cites Multi-granularity Correspondence Learning from Long-term Noisy Videos.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Multi-granularity Correspondence Learning from Long-term Noisy Videos

Reference 23

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This paper cites Mode: Clip data experts via clustering.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Mode: Clip data experts via clustering

Reference 24

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Observation 89848d56-5d4b-40f4-8cb3-8dec50ef2ba0 · outbound

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Joint wasserstein autoencoders for aligning multi- modal embeddings

Reference 25

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Observation 07e86a38-793b-4887-b26a-94737aff4b36 · outbound

This paper cites Do Concept Bottleneck Models Learn as Intended?.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Do Concept Bottleneck Models Learn as Intended?

Reference 26

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This paper cites [re] on the reproducibil- ity of post-hoc concept bottleneck models.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models [re] on the reproducibil- ity of post-hoc concept bottleneck models

Reference 27

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Observation 7d470b91-865e-4911-aa9d-6696bd5f7c44 · outbound

This paper cites A comprehensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models A comprehensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes

Reference 28

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Observation 51e46017-5258-43f3-b39a-0d7d5c13db8f · outbound

This paper cites Text2concept: Concept activation vectors di- rectly from text.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Text2concept: Concept activation vectors di- rectly from text

Reference 29

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Observation 82d9ca40-f113-4f9b-aef1-02cdf97cca54 · outbound

This paper cites Attention-based Joint Detection of Object and Semantic Part.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Attention-based Joint Detection of Object and Semantic Part

Reference 30

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Label-Free Concept Bottleneck Models

Reference 31

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

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models DINOv2: Learning Robust Visual Features without Supervision

Reference 32

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

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Learning transferable visual models from natural language supervi- sion

Reference 33

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This paper cites Do vision trans- formers see like convolutional neural networks? Advances in neural information processing systems, 34:12116–12128,.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Do vision trans- formers see like convolutional neural networks? Advances in neural information processing systems, 34:12116–12128,

Reference 34

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This paper cites Do concept bottleneck models obey locality? In XAI in Action: Past, Present, and Future Applications ,.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Do concept bottleneck models obey locality? In XAI in Action: Past, Present, and Future Applications ,

Reference 35

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This paper cites Optimal transport for multi-source domain adaptation under target shift.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Optimal transport for multi-source domain adaptation under target shift

Reference 36

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

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 37

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This paper cites Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning

Reference 38

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Auxiliary losses for learning generalizable concept-based models

Reference 39

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This paper cites A closer look at the intervention procedure of concept bot- tleneck models.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models A closer look at the intervention procedure of concept bot- tleneck models

Reference 40

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models The caltech-ucsd birds-200-2011 dataset

Reference 41

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation

Reference 42

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Zero-shot learning—a comprehensive eval- uation of the good, the bad and the ugly

Reference 43

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Attribute prototype network for zero-shot learning

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Language in a bottle: Language model guided concept bottlenecks for interpretable image classification

Reference 45

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Post-hoc Concept Bottleneck Models

Reference 46

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Pre-trained Vision-Language Models Learn Discoverable Visual Concepts

Reference 47

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off

Reference 48

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Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Conzic: Controllable zero-shot image captioning by sampling-based polishing

Reference 49

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This paper cites Meacap: Memory-augmented zero- shot image captioning.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Meacap: Memory-augmented zero- shot image captioning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:26:48.990928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6b26975c-755b-415f-95a1-2ede30390d14 · outbound

This paper cites Spda-cnn: Unifying semantic part detection and abstraction for fine-grained recognition.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Spda-cnn: Unifying semantic part detection and abstraction for fine-grained recognition

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:26:48.975265Z

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Observation dd9d49a7-f5a8-48e3-a14c-4ea687817366 · outbound

This paper cites Label distribution learning by optimal transport.

Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models Label distribution learning by optimal transport

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:26:48.960199Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 04974366-0115-44fe-9554-99f110885b9f · inbound

The SuperActivator Mechanism: Transformers Concentrate Reliable Concept Signals in the Tail cites this paper.

The SuperActivator Mechanism: Transformers Concentrate Reliable Concept Signals in the Tail Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-03T18:33:09.125847Z

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