Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models | Paper Citation Record

Observation 02eec673-95ec-4848-9ed7-c723164bb21d · outbound

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

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models An image is worth 16x16 words: Transformers for image recognition at scale

Reference 1

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Observation 8dc28dc4-4cf7-492a-816c-25ce460b6922 · outbound

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

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Swin transformer: Hierarchical vision transformer using shifted windows

Reference 2

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Observation 73111729-0e2c-4dbb-ac4d-a45ff0eefd15 · outbound

This paper cites Visual recognition with deep nearest centroids.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Visual recognition with deep nearest centroids

Reference 3

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Observation 2fe18a64-db0e-40d8-87e1-6b6fc029f3c8 · outbound

This paper cites Visual prompt tuning.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Visual prompt tuning

Reference 4

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Observation 6d743e87-a651-4302-8f89-aa7eb7d711e7 · outbound

This paper cites All you need is one: Capsule prompt tuning with a single vector.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models All you need is one: Capsule prompt tuning with a single vector

Reference 5

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Observation 545887ed-bb24-47fb-9f63-cdc18c5fcd78 · outbound

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

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models FOCUS: Fused Observation of Channels for Unveiling Spectra

Reference 6

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Observation 6bad0f8e-656a-40e5-a9db-c176723d5a5e · outbound

This paper cites Self-supervised visual prompting for cross-domain road damage detection.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Self-supervised visual prompting for cross-domain road damage detection

Reference 7

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Observation b6bc5f74-35c0-4826-a449-a65d5b1085e9 · outbound

This paper cites Do vision transformers see like convolutional neural networks? Advances in neural information processing systems, 34: 12116–12128, 2021.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Do vision transformers see like convolutional neural networks? Advances in neural information processing systems, 34: 12116–12128, 2021

Reference 8

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Observation 8134ac21-3068-4134-bed2-68e15887de44 · outbound

This paper cites Layer by layer: Uncovering hidden representations in language models.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Layer by layer: Uncovering hidden representations in language models

Reference 9

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Observation e15ecae1-463c-44c4-9eab-ffa786b945e2 · outbound

This paper cites Dispersion loss counteracts embedding condensation and improves generalization in small language models.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Dispersion loss counteracts embedding condensation and improves generalization in small language models

Reference 10

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Observation c1afd4c7-0909-428a-a671-8a4e92ef764e · outbound

This paper cites The information bottleneck method.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models The information bottleneck method

Reference 11

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Observation 82d98be5-df33-4f45-9684-4859b7140a99 · outbound

This paper cites A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-04T15:49:56.314788Z

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Observation 7122de9b-bd04-42fd-ad26-878bc6c828cf · outbound

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

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models The caltech-ucsd birds-200-2011 dataset

Reference 13

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Observation e2ae869f-30e1-4e85-9486-eba5f696f3df · outbound

This paper cites Diversity-aware meta visual prompting.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Diversity-aware meta visual prompting

Reference 14

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Observation e3893ed0-a159-46c2-a771-a7a965882077 · outbound

This paper cites Darts: Differentiable architecture search.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Darts: Differentiable architecture search

Reference 15

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Observation 6dd5640b-013e-4be7-9d22-05ff7e2c775f · outbound

This paper cites Convex optimization.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Convex optimization

Reference 16

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Observation db12f776-a68e-4087-9995-43f3c20c5ed3 · outbound

This paper cites Shannon entropy, renyi entropy, and information.Statistics and Inf.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Shannon entropy, renyi entropy, and information.Statistics and Inf

Reference 17

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Observation a358a85f-135e-4317-9c23-71d2c99c6bd8 · outbound

This paper cites Deep residual learning for image recognition.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Deep residual learning for image recognition

Reference 18

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Observation 30b6c9d2-d434-497f-acbc-3d6263db4007 · outbound

This paper cites Film: Visual reasoning with a general conditioning layer.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Film: Visual reasoning with a general conditioning layer

Reference 19

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Observation 2533605a-e36d-474d-b04e-15fb7c6f3a90 · outbound

This paper cites Attention is all you need.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Attention is all you need

Reference 20

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Observation 0f975b65-1ab3-461f-9731-9fa4a64f71b6 · outbound

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

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Automated flower classification over a large number of classes

Reference 21

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Observation 1924201a-0496-41d2-ad06-203169d58b17 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Masked autoencoders are scalable vision learners

Reference 22

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Observation ec308675-5944-4b70-b569-f24f1c8c53d0 · outbound

This paper cites An empirical study of training self-supervised vision transformers.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models An empirical study of training self-supervised vision transformers

Reference 23

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Observation 96f33c4b-3d86-4fbf-a5b7-e849ba5cc8f6 · outbound

This paper cites How well do sparse imagenet models transfer? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12266–12276, 2022.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models How well do sparse imagenet models transfer? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12266–12276, 2022

Reference 24

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Observation d4fd197b-e33c-404e-a982-80d949367e13 · outbound

This paper cites How transferable are features in deep neural networks? Advances in neural information processing systems, 27, 2014.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models How transferable are features in deep neural networks? Advances in neural information processing systems, 27, 2014

Reference 25

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Observation 6b73fbb1-7e8e-42b4-b042-a9ebfa9a6ced · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Improved Baselines with Momentum Contrastive Learning

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-04T15:49:56.342852Z

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Observation 22ac06bf-c721-477d-9c67-ba9bdef6c64f · outbound

This paper cites Side-tuning: a baseline for network adaptation via additive side networks.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Side-tuning: a baseline for network adaptation via additive side networks

Reference 27

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Observation d9ae52e4-3a1f-49ab-a16a-82df7ff1d17a · outbound

This paper cites Learning multiple visual domains with residual adapters.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Learning multiple visual domains with residual adapters

Reference 28

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Observation dbf78982-33df-48ea-93f1-166ced51327d · outbound

This paper cites Tinytl: Reduce memory, not parameters for efficient on-device learning.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Tinytl: Reduce memory, not parameters for efficient on-device learning

Reference 29

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Observation f56fbdb1-4aa8-49bd-a357-9357ac4ecbd4 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Lora: Low-rank adaptation of large language models

Reference 30

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Observation e19ded3a-e778-4e7b-b2d6-c72842cb8709 · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recognition.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 31

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Observation b8f42c0f-f310-4e6d-b4ff-47065196011a · outbound

This paper cites Efficient adaptation of large vision transformer via adapter re-composing.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Efficient adaptation of large vision transformer via adapter re-composing

Reference 32

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Observation f0396b2a-2449-4ac6-b1d4-b73136b6a8dc · outbound

This paper cites E 2 vpt: An effective and efficient approach for visual prompt tuning.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models E 2 vpt: An effective and efficient approach for visual prompt tuning

Reference 33

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Observation 5936f959-c278-444d-aab3-4daf25603e5a · outbound

This paper cites Learning expressive prompting with residuals for vision transformers.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Learning expressive prompting with residuals for vision transformers

Reference 34

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Observation b085020d-1328-4290-b5dd-f1fb1347e2d4 · outbound

This paper cites Sa 2vp: Spatially aligned- and-adapted visual prompt.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Sa 2vp: Spatially aligned- and-adapted visual prompt

Reference 35

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Observation 514e512b-804c-485f-929f-3e1d9a8fc474 · outbound

This paper cites Revisiting the power of prompt for visual tuning.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Revisiting the power of prompt for visual tuning

Reference 36

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Observation 1c616ac9-1af1-4e5c-b274-c1f5e5acbc45 · outbound

This paper cites Visual fourier prompt tuning.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Visual fourier prompt tuning

Reference 37

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Observation fdd790f8-86f4-4e74-88be-6340c4c4b13c · outbound

This paper cites Lor-vp: Low-rank visual prompting for efficient vision model adaptation.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Lor-vp: Low-rank visual prompting for efficient vision model adaptation

Reference 38

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Observation 32264fd3-342f-4cf2-8bab-114e1826edc3 · outbound

This paper cites Da-vpt: Semantic-guided visual prompt tuning for vision transformers.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Da-vpt: Semantic-guided visual prompt tuning for vision transformers

Reference 39

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Observation f8ca867e-101d-466e-9f94-54a7005685ff · outbound

This paper cites Improving visual prompt tuning for self-supervised vision transformers.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Improving visual prompt tuning for self-supervised vision transformers

Reference 40

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Observation 471dcb08-4089-4f16-b02b-f6e91b0d0eee · outbound

This paper cites Facing the elephant in the room: Visual prompt tuning or full finetuning? InThe TwelfthInternational Conference on Learning Representations.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Facing the elephant in the room: Visual prompt tuning or full finetuning? InThe TwelfthInternational Conference on Learning Representations

Reference 41

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Observation 6dadc7fb-ce44-41ab-be5b-17fda95acd86 · outbound

This paper cites Rethinking spatial dimensions of vision transformers.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Rethinking spatial dimensions of vision transformers

Reference 42

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Observation 3c72e6d1-4087-4451-adb4-ad7670b265f3 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Parameter-efficient transfer learning for nlp

Reference 43

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Observation 157f5af7-dc8f-40e9-b5c0-d3d20d7c3340 · outbound

This paper cites Adapterhub: A framework for adapting transformers.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Adapterhub: A framework for adapting transformers

Reference 44

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Observation 33aa5c06-7a9c-42c9-aa3e-15c157b39017 · outbound

This paper cites Compacter: Efficient low-rank hypercomplex adapter layers.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Compacter: Efficient low-rank hypercomplex adapter layers

Reference 45

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Observation 48173aba-1bea-4084-a6d8-ff770b287db0 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Prefix-tuning: Optimizing continuous prompts for generation

Reference 46

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Observation 52d842b5-b0c3-49cb-b331-15ce60299255 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models The power of scale for parameter-efficient prompt tuning

Reference 47

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Observation 65771cdb-1b64-4e0e-893e-cc7613097d0d · outbound

This paper cites Autoprompt: Eliciting knowledge from language models with automatically generated prompts.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Autoprompt: Eliciting knowledge from language models with automatically generated prompts

Reference 48

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Observation 84cc1709-4a04-4865-a023-a9e7d4e33da9 · outbound

This paper cites Universal adversarial triggers for attacking and analyzing nlp.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Universal adversarial triggers for attacking and analyzing nlp

Reference 49

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Observation de1baeff-96e5-4e56-82d0-362c2e14709d · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Qlora: Efficient finetuning of quantized llms

Reference 50

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Observation 86650252-7cef-4148-898c-1eec862dfb3a · outbound

This paper cites Bitfit: Simple parameter-efficient fine-tuning for transformer- based masked language-models.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Bitfit: Simple parameter-efficient fine-tuning for transformer- based masked language-models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation ea5233a1-bc63-43d0-b771-690ea0db3a04 · outbound

This paper cites Prompt learns prompt: Exploring knowledge- aware generative prompt collaboration for video captioning.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Prompt learns prompt: Exploring knowledge- aware generative prompt collaboration for video captioning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation d4875d43-800a-4f7a-88d2-c2c8c11036a4 · outbound

This paper cites Visual instance-aware prompt tuning.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Visual instance-aware prompt tuning

Reference 53

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Observation 78899d35-5ae8-4c5d-bf14-87cc3919893a · outbound

This paper cites Visual Variational Autoencoder Prompt Tuning.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Visual Variational Autoencoder Prompt Tuning

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:49:56.359808Z

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Observation 53142d66-ee38-4ead-b9a0-10485dd4259d · outbound

This paper cites Pro-vpt: Distribution-adaptive visual prompt tuning via prompt relocation.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Pro-vpt: Distribution-adaptive visual prompt tuning via prompt relocation

Reference 55

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation 7551ac01-bd74-4a58-8edc-a9a6ccb4549a · outbound

This paper cites Cvpt: Cross visual prompt tuning.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Cvpt: Cross visual prompt tuning

Reference 56

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Observation 9c74411e-2211-4a37-ad77-0ffd1d3be7d0 · outbound

This paper cites Neural architecture search: A survey.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Neural architecture search: A survey

Reference 57

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Observation 7ffffb4b-2cf0-4c81-aa8c-cae8c9a4ec9b · outbound

This paper cites Autoformer: Searching transformers for visual recognition.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Autoformer: Searching transformers for visual recognition

Reference 58

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Observation 954fcd1a-f0c5-40dd-89c7-2b4f75b08e2c · outbound

This paper cites Vitas: Vision transformer architecture search.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Vitas: Vision transformer architecture search

Reference 59

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Observation c13d4488-7707-405f-adcf-d2f7e1bf0cfc · outbound

This paper cites Nasvit: Neural architecture search for efficient vision transformers with gradient conflict aware supernet training.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Nasvit: Neural architecture search for efficient vision transformers with gradient conflict aware supernet training

Reference 60

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Observation 768b285d-6a13-4269-89fe-1019efd2e517 · outbound

This paper cites Dynamicvit: Efficient vision transformers with dynamic token sparsification.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Dynamicvit: Efficient vision transformers with dynamic token sparsification

Reference 61

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation ed2e4629-04d0-478e-9006-4c0093375708 · outbound

This paper cites Evit: Expediting vision transformers via token reorganizations.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Evit: Expediting vision transformers via token reorganizations

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation 52c7ceed-c6a0-45e3-b11c-cace561507eb · outbound

This paper cites Advancing textual prompt learning with anchored attributes.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Advancing textual prompt learning with anchored attributes

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation 85e845f2-aeeb-45ab-a9e4-08e1eec2bfac · outbound

This paper cites Differentiable prompt learning for vision language models.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Differentiable prompt learning for vision language models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation f1e8ba9a-e643-4639-bb11-89bde126f004 · outbound

This paper cites Causation, prediction, and search, volume 81.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Causation, prediction, and search, volume 81

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation f4538443-b729-433a-9ff3-bdf0d51ba9e3 · outbound

This paper cites Prompt-based adaptation in large- scale vision models: A survey.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Prompt-based adaptation in large- scale vision models: A survey

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation 766750cd-e767-4bca-9a6c-87670316aedf · outbound

This paper cites Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-07-04T15:49:56.336184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e86da344-6803-4105-badc-bdbc053032ab · outbound

This paper cites 2510.02630 , archivePrefix=.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models 2510.02630 , archivePrefix=

Reference 68

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T15:49:56.354148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation eae83ffa-abf4-4904-9a51-8e92360e30ae · outbound

This paper cites Ctr-lora: Curvature-aware and trust-region guided low-rank adaptation for large language models.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Ctr-lora: Curvature-aware and trust-region guided low-rank adaptation for large language models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation 9f8e0147-3310-4849-bac4-e156dba06903 · outbound

This paper cites Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:49:56.365368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4a1d2452-0f02-48d9-a355-25e350da1239 · outbound

This paper cites Prime once, then reprogram locally: An efficient alternative to black-box service model adaptation.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Prime once, then reprogram locally: An efficient alternative to black-box service model adaptation

Reference 71

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation dfe7dfde-62b8-4ed2-9c17-c6c548d2f3b8 · outbound

This paper cites Dpcore: Dynamic prompt coreset for continual test-time adaptation.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Dpcore: Dynamic prompt coreset for continual test-time adaptation

Reference 72

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation da33d525-05b0-4cb1-aa98-4b9dbc3aec45 · outbound

This paper cites Flasheval: Towards fast and accurate evaluation of text-to-image diffusion generative models.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Flasheval: Towards fast and accurate evaluation of text-to-image diffusion generative models

Reference 73

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation dc30faf3-5be6-47e8-93ec-707998913a1d · outbound

This paper cites Thinimg: Cross-modal steganography for presenting talking heads in images.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Thinimg: Cross-modal steganography for presenting talking heads in images

Reference 74

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation e96c0261-c8d3-44c6-ace4-42cc8dce9ff2 · outbound

This paper cites Streamvlo: Streaming visual-lidar odometry with cumulative drift compensation.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Streamvlo: Streaming visual-lidar odometry with cumulative drift compensation

Reference 75

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation cf9a880f-808f-4e6c-90fb-ca1bbb819704 · outbound

This paper cites DriveVA: Video Action Models are Zero-Shot Drivers.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models DriveVA: Video Action Models are Zero-Shot Drivers

Reference 76

Resolution: verified exact; local_arxiv, observed 2026-07-04T15:49:56.326328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f50e39c7-fd8f-4e18-90df-bf031510cd77 · outbound

This paper cites Unsupervised hyperspectral image super-resolution via self-supervised modality decoupling.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Unsupervised hyperspectral image super-resolution via self-supervised modality decoupling

Reference 77

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation 14045434-a1b7-42ca-af10-cf2b165e21f3 · outbound

This paper cites Pansharpening for thin-cloud contaminated remote sensing images: a unified framework and benchmark dataset.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Pansharpening for thin-cloud contaminated remote sensing images: a unified framework and benchmark dataset

Reference 78

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation be21c393-2e77-4044-aa12-0e4208f45d05 · outbound

This paper cites Frequency-decoupled learning for joint thin-cloud removal and pansharpening.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Frequency-decoupled learning for joint thin-cloud removal and pansharpening

Reference 79

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation 115c913b-18c6-477b-ab7b-1e2829153aab · outbound

This paper cites Semi-supervised semantic segmentation with multi-constraint consistency learning.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Semi-supervised semantic segmentation with multi-constraint consistency learning

Reference 80

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation f3322269-b8e3-4d52-b826-3bcbf0b20788 · outbound

This paper cites Depmatch: Boosting semi-supervised semantic segmentation by exploring depth difference knowledge.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Depmatch: Boosting semi-supervised semantic segmentation by exploring depth difference knowledge

Reference 81

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation 456e217f-be52-4420-b04b-ee7d9404666c · outbound

This paper cites Pca-seg: Revisiting cost aggregation for open-vocabulary semantic and part segmentation.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Pca-seg: Revisiting cost aggregation for open-vocabulary semantic and part segmentation

Reference 82

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

Source-reported events for the cited work

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Observation 7558d2ff-5f0a-4438-a4fc-994bcf620c62 · outbound

This paper cites an unresolved cited work.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Unresolved cited work

Reference 83

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

Source-reported events for the cited work

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Observation 7774712d-3bc9-446e-9364-ea4cb33181d2 · outbound

This paper cites an unresolved cited work.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Unresolved cited work

Reference 84

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

Source-reported events for the cited work

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Observation 4c221815-c0e7-4123-b478-268459f47346 · outbound

This paper cites This avoids explicit Hessian materialization; memory overhead comes from retaining the graph for the second pass.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models This avoids explicit Hessian materialization; memory overhead comes from retaining the graph for the second pass

Reference 85

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

Source-reported events for the cited work

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Observation ef31f980-5398-4830-9aee-043472f0cfb9 · outbound

This paper cites (5) in the main paper that the affine parameters γ, β are generated via MLPs ϕt(s) taking the prompt summary s∈R d as input.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models (5) in the main paper that the affine parameters γ, β are generated via MLPs ϕt(s) taking the prompt summary s∈R d as input

Reference 86

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation 117f2042-3ca4-4700-a34c-479c4dc5cdb0 · outbound

This paper cites However, standard cross-attention consumes excessive parameters (3×d 2 ≈1.8M).

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models However, standard cross-attention consumes excessive parameters (3×d 2 ≈1.8M)

Reference 87

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

Source-reported events for the cited work

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Observation 22bd8223-31ca-4b01-b66b-983c37cb8102 · outbound

This paper cites Layer- specific.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Layer- specific

Reference 88

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

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Observation 2f4eac90-90fb-420e-b068-42692a3ad536 · outbound

This paper cites Affine" and.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Affine" and

Reference 89

Resolution: unresolved; no resolver link, observed 2026-06-26T01:27:24.402364Z

Source-reported events for the cited work

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Observation 95c3b2bd-aff5-44a6-b337-96d326b01709 · outbound

This paper cites Collapse Rate.

Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models Collapse Rate

Reference 90

Resolution: malformed identifier; no resolver link, observed 2026-06-26T01:27:24.402364Z

Source-reported events for the cited work

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