Paper Citation Record · LEDGER

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion

As of 9 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 5 inbound Pith citation observations for arXiv:2502.04263.

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

pith.paper-citation-record.v1
2502.04263 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:05:28.366500Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:01:24.087885Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T14:13:29.951494Z

Reference resolution

61 of 61 outbound references displayed

verified exact1
verified fuzzy21
unresolved39
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8e67da87-1f65-4a36-9c84-2ed7d83ab3bc · outbound

This paper cites write newline.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion write newline

Reference 1

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Observation df186c1b-5f45-42da-ae80-8a73e10a113e · outbound

This paper cites iSEARLE: Improving Textual Inversion for Zero-Shot Composed Image Retrieval.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion iSEARLE: Improving Textual Inversion for Zero-Shot Composed Image Retrieval

Reference 2

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Observation 6a124118-1d9a-4700-8267-fe7b4076ded6 · outbound

This paper cites nocaps: novel object captioning at scale.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion nocaps: novel object captioning at scale

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:05:31.802665Z

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Observation 2b8ea1ac-88fe-458b-a1f0-5357f4f48c1e · outbound

This paper cites Zero-Shot Composed Image Retrieval with Textual Inversion.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Zero-Shot Composed Image Retrieval with Textual Inversion

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:05:31.792661Z

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Observation e24f9d0c-9719-4aea-bc9e-27794b8a251a · outbound

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Food-101--mining discriminative components with random forests

Reference 5

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Observation ff880db6-7a66-46f4-bf95-40dda6bfbad5 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion A simple framework for contrastive learning of visual representations

Reference 6

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Observation 320cc0d9-1416-4192-bc80-9746e1222e7d · outbound

This paper cites Describing textures in the wild.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Describing textures in the wild

Reference 7

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Observation b34db35c-6e3d-4e2c-887f-31bb92027d55 · outbound

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Imagenet: A large-scale hierarchical image database

Reference 8

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Observation f4ebc51a-6dc0-4995-9654-972d35bd0975 · outbound

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

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Observation 4d93ccfe-64ce-41ec-ab1a-36897b528558 · outbound

This paper cites The Llama 3 Herd of Models.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion The Llama 3 Herd of Models

Reference 10

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Observation b092ac40-efb0-402d-93e4-5b1ee8646544 · outbound

This paper cites Asirra: a captcha that exploits interest-aligned manual image categorization.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Asirra: a captcha that exploits interest-aligned manual image categorization

Reference 11

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Observation f16a91e5-fbf0-4781-a47f-eeae517f014e · outbound

This paper cites Structure and content-guided video synthesis with diffusion models.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Structure and content-guided video synthesis with diffusion models

Reference 12

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Observation d639af3c-d7a2-4186-a0a7-ab65a62c1bf1 · outbound

This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

Reference 13

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Observation 8fe38a14-23d5-424a-ba2d-e2223f2e2a5d · outbound

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Datacomp: In search of the next generation of multimodal datasets

Reference 14

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Observation ad8985fd-34e1-4311-8b00-0f681239b6b8 · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 15

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Observation 63a55951-2600-47fd-b55b-351820567691 · outbound

This paper cites Towards flexible perception with visual memory.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Towards flexible perception with visual memory

Reference 16

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Observation 38e593f4-eeec-400b-a8f8-6acc321ef0dc · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 17

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Observation cedcd4ea-f5da-4c1e-86ab-a417dec8c4b9 · outbound

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Scaling up visual and vision-language representation learning with noisy text supervision

Reference 18

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Observation 6e08ae28-74fa-4ae2-ad32-1a8c14e0cbd5 · outbound

This paper cites Deep visual-semantic alignments for generating image descriptions.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Deep visual-semantic alignments for generating image descriptions

Reference 19

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Observation d2b1de36-b920-436e-b296-197dd7e96096 · outbound

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion 3d object representations for fine-grained categorization

Reference 20

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Observation 25920617-61a7-448d-bf15-b68c9858cfac · outbound

This paper cites Newsweeder: Learning to filter netnews.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Newsweeder: Learning to filter netnews

Reference 21

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Observation 6a1cca45-c804-4898-b714-2d95db06f8fb · outbound

This paper cites DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training

Reference 22

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Observation 7eaf461c-ec4e-4ac5-bf04-695ec715f0a5 · outbound

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm

Reference 23

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Observation dcffd5eb-efe3-4146-b392-479e4d08bcbe · outbound

This paper cites Mind the Gap: Understanding the Modality Gap in Multi-Modal Contrastive Representation Learning.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Mind the Gap: Understanding the Modality Gap in Multi-Modal Contrastive Representation Learning

Reference 24

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Observation 666c1b54-5e94-4533-8dc4-f61fa7837b40 · outbound

This paper cites Microsoft coco: Common objects in context.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Microsoft coco: Common objects in context

Reference 25

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This paper cites Visual instruction tuning.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Visual instruction tuning

Reference 26

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Observation c4a5a5ad-23f6-4684-9168-b3a6e7bac4a8 · outbound

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Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Decoupled weight decay regularization

Reference 27

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This paper cites Image segmentation using text and image prompts.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Image segmentation using text and image prompts

Reference 28

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Observation 5d68d289-afc7-4060-af7d-2faaa7000cb5 · outbound

This paper cites Learning word vectors for sentiment analysis.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Learning word vectors for sentiment analysis

Reference 29

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Observation 52dd46dc-1bfa-4692-aa54-33ba31143ca7 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Fine-Grained Visual Classification of Aircraft

Reference 30

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Observation ad756d82-f0fa-4950-8361-5aa369639e19 · outbound

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Slip: Self-supervision meets language-image pre-training

Reference 31

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Observation 30629513-27b0-4a96-a759-3b43540c0eb0 · outbound

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Automated flower classification over a large number of classes

Reference 32

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This paper cites Deep metric learning via lifted structured feature embedding.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Deep metric learning via lifted structured feature embedding

Reference 33

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This paper cites Clip-guided vision-language pre-training for question answering in 3d scenes.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Clip-guided vision-language pre-training for question answering in 3d scenes

Reference 34

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Observation 43f0499c-47ad-4c36-973b-d9908769a5c9 · outbound

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Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Cats and dogs

Reference 35

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This paper cites Eclipse: A resource-efficient text-to-image prior for image generations.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Eclipse: A resource-efficient text-to-image prior for image generations

Reference 36

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Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models

Reference 37

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This paper cites Revisiting oxford and paris: Large-scale image retrieval benchmarking.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Revisiting oxford and paris: Large-scale image retrieval benchmarking

Reference 38

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Observation 2d8504b3-ee9d-4792-8b83-9c299a793a3f · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Learning Transferable Visual Models From Natural Language Supervision

Reference 39

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Observation aea494b7-c64a-473e-a9f6-36c9950c2d25 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 40

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This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 41

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This paper cites Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models

Reference 42

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This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 43

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 44

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This paper cites Towards Understanding the Modality Gap in CLIP.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Towards Understanding the Modality Gap in CLIP

Reference 45

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This paper cites Improved deep metric learning with multi-class n-pair loss objective.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Improved deep metric learning with multi-class n-pair loss objective

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:05:31.534046Z

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Observation adad59bb-9f9f-406c-9c95-8386147827c9 · outbound

This paper cites CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment

Reference 47

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Observation edbcfd55-15c3-4edb-b6ed-461eb7da6929 · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 48

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Observation fac08640-37ec-48d0-b3ff-5667bca5d0d8 · outbound

This paper cites SuS-X: Training-Free Name-Only Transfer of Vision-Language Models.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion SuS-X: Training-Free Name-Only Transfer of Vision-Language Models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:05:31.524274Z

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Observation 5efc861e-25af-419d-b972-9ab6a20fc525 · outbound

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion The caltech-ucsd birds-200-2011 dataset

Reference 50

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Observation 53627af5-6126-4204-a195-5dbaf1e5e96b · outbound

This paper cites Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:05:31.508001Z

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Observation 11fbbb18-74b0-4e16-a09f-43f52b2f9afd · outbound

This paper cites Coca: Contrastive captioners are image-text foundation models.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Coca: Contrastive captioners are image-text foundation models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:05:31.497851Z

Source-reported events for the cited work

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Observation 692e86c5-9045-40d1-9ca3-544cc62727a8 · outbound

This paper cites Lit: Zero-shot transfer with locked-image text tuning.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Lit: Zero-shot transfer with locked-image text tuning

Reference 53

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Observation dc84c6d1-b7e2-415e-8467-e0ebbee1d78a · outbound

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Sigmoid loss for language image pre-training

Reference 54

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Observation 64c93fef-c480-4d13-886b-c78aefefe651 · outbound

This paper cites Diagnosing and Rectifying Vision Models using Language.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Diagnosing and Rectifying Vision Models using Language

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:05:31.471591Z

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This paper cites Avid: Any-length video inpainting with diffusion model.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Avid: Any-length video inpainting with diffusion model

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:05:31.461236Z

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Observation 5538da71-045c-4d56-bdb5-3b5ca66cb48f · outbound

This paper cites Extract free dense labels from clip.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Extract free dense labels from clip

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:05:31.451094Z

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Observation 7847be78-e3e5-45a1-b39d-08130c794dbb · outbound

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

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Learning to prompt for vision-language models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T23:05:28.355537Z

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This paper cites @esa (Ref.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion @esa (Ref

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T23:05:28.358826Z

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Observation 2965186f-a05c-4125-a819-6c8c989c558c · outbound

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Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion Unresolved cited work

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-08T23:05:28.362890Z

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Observation 647fabc1-5e1a-4907-aeef-00cde2700bf4 · outbound

This paper cites a photo of.

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion a photo of

Reference 61

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Pith citing papers

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Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models cites this paper.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion

Reference 22

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Observation a3bd064c-3c80-479b-a405-5813f35c1596 · inbound

Global and Local Entailment Learning for Natural World Imagery cites this paper.

Global and Local Entailment Learning for Natural World Imagery Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion

Reference 24

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Observation f64889f6-b20e-45ba-98ec-b6bcaf0766dd · inbound

Mind the Gap: Preserving and Compensating for the Modality Gap in CLIP-Based Continual Learning cites this paper.

Mind the Gap: Preserving and Compensating for the Modality Gap in CLIP-Based Continual Learning Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion

Reference 26

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Best Segmentation Buddies for Image-Shape Correspondence cites this paper.

Best Segmentation Buddies for Image-Shape Correspondence Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion

Reference 38

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Observation 963adb28-36cf-4c0d-894e-b1dc113a3e37 · inbound

LAST: Bridging Vision-Language and Action Manifolds via Gromov-Wasserstein Alignment cites this paper.

LAST: Bridging Vision-Language and Action Manifolds via Gromov-Wasserstein Alignment Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion

Reference 7

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