Improving Personalized Search with Regularized Low-Rank Parameter Updates | Paper Citation Record

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This paper cites Myvlm: Personalizing vlms for user-specific queries.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Myvlm: Personalizing vlms for user-specific queries

Reference 1

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Observation 40429e07-67fa-4de4-aebe-debf2ce6b2de · outbound

This paper cites Conditioned and composed image retrieval combining and partially fine-tuning clip-based features.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Conditioned and composed image retrieval combining and partially fine-tuning clip-based features

Reference 2

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This paper cites Effective conditioned and composed im- age retrieval combining clip-based features.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Effective conditioned and composed im- age retrieval combining clip-based features

Reference 3

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This paper cites Zero-shot composed image retrieval with textual inversion.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Zero-shot composed image retrieval with textual inversion

Reference 4

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Observation d443d39b-96bf-4256-8ca7-8228d609ec3a · outbound

This paper cites Lasp: Text-to- text optimization for language-aware soft prompting of vi- sion & language models.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Lasp: Text-to- text optimization for language-aware soft prompting of vi- sion & language models

Reference 5

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Observation 189352ca-c512-4d54-91b6-7de8e4ee87a1 · outbound

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

Improving Personalized Search with Regularized Low-Rank Parameter Updates PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

Reference 6

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This paper cites this is my unicorn, fluffy.

Improving Personalized Search with Regularized Low-Rank Parameter Updates this is my unicorn, fluffy

Reference 7

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This paper cites Variational prompt tuning improves generalization of vision-language models.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Variational prompt tuning improves generalization of vision-language models

Reference 8

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Observation a5109ce6-263f-4aa6-998f-5d5d2121fae9 · outbound

This paper cites DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter.

Improving Personalized Search with Regularized Low-Rank Parameter Updates DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter

Reference 9

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

Improving Personalized Search with Regularized Low-Rank Parameter Updates An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 10

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This paper cites Efros, and Jacob Steinhardt.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Efros, and Jacob Steinhardt

Reference 11

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Observation a891cc64-f30d-4cc7-b249-4de4c042fbbf · outbound

This paper cites Clip-adapter: Better vision-language models with feature adapters.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Clip-adapter: Better vision-language models with feature adapters

Reference 12

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Observation 5b42d8fd-e9f3-423d-9aa0-0eee6adbdb28 · outbound

This paper cites Deepfashion2: A versatile benchmark for de- tection, pose estimation, segmentation and re-identification of clothing images.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Deepfashion2: A versatile benchmark for de- tection, pose estimation, segmentation and re-identification of clothing images

Reference 13

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This paper cites Personal- ized residuals for concept-driven text-to-image generation.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Personal- ized residuals for concept-driven text-to-image generation

Reference 14

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This paper cites Svdiff: Compact param- eter space for diffusion fine-tuning.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Svdiff: Compact param- eter space for diffusion fine-tuning

Reference 15

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Improving Personalized Search with Regularized Low-Rank Parameter Updates LoRA: Low-Rank Adaptation of Large Language Models

Reference 16

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This paper cites Maple: Multi-modal prompt learning.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Maple: Multi-modal prompt learning

Reference 17

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This paper cites Personalised clip or: how to find your vacation videos.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Personalised clip or: how to find your vacation videos

Reference 18

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Observation 8d5bff8c-21dd-443d-80c8-704daaf3c5d5 · outbound

This paper cites Multi-concept customization of text-to-image diffusion.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Multi-concept customization of text-to-image diffusion

Reference 19

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Observation 0cd7a0d1-e450-4cdc-b0ce-92aaca5b3628 · outbound

This paper cites Read-only prompt optimization for vision-language few-shot learning.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Read-only prompt optimization for vision-language few-shot learning

Reference 20

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Observation 12767cce-b7f0-4b72-a26f-9ee19c25b019 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Improving Personalized Search with Regularized Low-Rank Parameter Updates The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 21

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This paper cites Visual instruction tuning.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Visual instruction tuning

Reference 22

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This paper cites Prompt distribution learning.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Prompt distribution learning

Reference 23

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Observation 53c98b37-6707-4d0f-91ac-45d3d01c2021 · outbound

This paper cites NewMove: Customizing text-to-video models with novel motions.

Improving Personalized Search with Regularized Low-Rank Parameter Updates NewMove: Customizing text-to-video models with novel motions

Reference 24

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Observation f3343424-e559-48a3-930d-49ed73ae6d87 · outbound

This paper cites Yo'LLaVA: Your Personalized Language and Vision Assistant.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Yo'LLaVA: Your Personalized Language and Vision Assistant

Reference 25

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Observation 1fc4b271-2de7-4a5b-b0c6-ae5c1a069082 · outbound

This paper cites Orthogonal adaptation for modular customization of diffusion models.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Orthogonal adaptation for modular customization of diffusion models

Reference 26

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Observation 9ea51d0f-934f-4ff5-89f7-ca679f37232d · outbound

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

Improving Personalized Search with Regularized Low-Rank Parameter Updates Learning transferable visual models from natural language supervision

Reference 27

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This paper cites Concon-chi: Concept-context chimera benchmark for personalized vision-language tasks.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Concon-chi: Concept-context chimera benchmark for personalized vision-language tasks

Reference 28

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

Improving Personalized Search with Regularized Low-Rank Parameter Updates Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 29

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This paper cites Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models

Reference 30

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Improving Personalized Search with Regularized Low-Rank Parameter Updates Pic2word: Mapping pictures to words for zero-shot composed image retrieval

Reference 31

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This paper cites Collie: Continual learning of language grounding from language-image em- beddings.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Collie: Continual learning of language grounding from language-image em- beddings

Reference 32

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This paper cites Key-locked rank one editing for text-to-image personaliza- tion.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Key-locked rank one editing for text-to-image personaliza- tion

Reference 33

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Improving Personalized Search with Regularized Low-Rank Parameter Updates Multimodal few-shot learning with frozen language models

Reference 34

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Improving Personalized Search with Regularized Low-Rank Parameter Updates Composing text and image for image retrieval-an empirical odyssey

Reference 35

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This paper cites Dual modality prompt tuning for vision-language pre-trained model.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Dual modality prompt tuning for vision-language pre-trained model

Reference 36

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Improving Personalized Search with Regularized Low-Rank Parameter Updates Visual- language prompt tuning with knowledge-guided context op- timization

Reference 37

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Improving Personalized Search with Regularized Low-Rank Parameter Updates Meta-personalizing vision- language models to find named instances in video

Reference 38

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This paper cites From image descriptions to visual denotations: New similarity metrics for semantic inference over event descrip- tions.

Improving Personalized Search with Regularized Low-Rank Parameter Updates From image descriptions to visual denotations: New similarity metrics for semantic inference over event descrip- tions

Reference 39

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This paper cites Inserting Anybody in Diffusion Models via Celeb Basis.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Inserting Anybody in Diffusion Models via Celeb Basis

Reference 40

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This paper cites Tip- adapter: Training-free adaption of clip for few-shot classi- fication.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Tip- adapter: Training-free adaption of clip for few-shot classi- fication

Reference 41

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This paper cites Conditional prompt learning for vision-language mod- els.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Conditional prompt learning for vision-language mod- els

Reference 42

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Improving Personalized Search with Regularized Low-Rank Parameter Updates Learning to prompt for vision-language models

Reference 43

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This paper cites Prompt-aligned gradient for prompt tuning.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Prompt-aligned gradient for prompt tuning

Reference 44

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This paper cites However unlike zero-shot methods like SEARLE, we use these 3 validation concepts to select the λ regularization hyperparameter.

Improving Personalized Search with Regularized Low-Rank Parameter Updates However unlike zero-shot methods like SEARLE, we use these 3 validation concepts to select the λ regularization hyperparameter

Reference 45

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This paper cites 10 with 5 randomly selected train images for each concept per run.

Improving Personalized Search with Regularized Low-Rank Parameter Updates 10 with 5 randomly selected train images for each concept per run

Reference 46

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This paper cites 11, LoRA layers (Tab.

Improving Personalized Search with Regularized Low-Rank Parameter Updates 11, LoRA layers (Tab

Reference 47

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This paper cites [38] Yeh et al.

Improving Personalized Search with Regularized Low-Rank Parameter Updates [38] Yeh et al

Reference 48

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This paper cites We report results for our method in this setting in Tab.

Improving Personalized Search with Regularized Low-Rank Parameter Updates We report results for our method in this setting in Tab

Reference 49

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Improving Personalized Search with Regularized Low-Rank Parameter Updates Unresolved cited work

Reference 50

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This paper cites puppet” as they are animal-like objects created from household mate- rials, but this is unlikely to align with CLIP’s concept of “puppet.

Improving Personalized Search with Regularized Low-Rank Parameter Updates puppet” as they are animal-like objects created from household mate- rials, but this is unlikely to align with CLIP’s concept of “puppet

Reference 51

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This paper cites Caption this image in 1-2 sentences.

Improving Personalized Search with Regularized Low-Rank Parameter Updates Caption this image in 1-2 sentences

Reference 52

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This paper cites This strategy is similar to weight decay, which also encourages learning small weights, but differs in two key aspects.

Improving Personalized Search with Regularized Low-Rank Parameter Updates This strategy is similar to weight decay, which also encourages learning small weights, but differs in two key aspects

Reference 53

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Improving Personalized Search with Regularized Low-Rank Parameter Updates Unresolved cited work

Reference 54

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Observation e828d000-972c-419a-96c7-91ef5d9a042a · outbound

This paper cites sks” in our main experiments. We observe that selecting a V ∗ for which CLIP likely has a strong existing representation (e.g., “dog.

Improving Personalized Search with Regularized Low-Rank Parameter Updates sks” in our main experiments. We observe that selecting a V ∗ for which CLIP likely has a strong existing representation (e.g., “dog

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:39:36.155114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers