MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models | Paper Citation Record

Observation 992e04dd-a6b2-41cf-8da8-5358ca445e37 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models On the Opportunities and Risks of Foundation Models

Reference 1

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Observation 0326633f-c968-4b36-a692-9ec17e49978d · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Learning transferable visual models from natural language supervision,

Reference 2

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Observation df8e4d00-85a0-4214-b987-d4cbf8e8b722 · outbound

This paper cites Deep residual learning for image recognition,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Deep residual learning for image recognition,

Reference 3

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Observation b01d33bd-a95a-4853-ab78-a7a0e1c130b2 · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 4

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Observation da35c657-7a4b-443c-9eeb-b42f3f8023bf · outbound

This paper cites arXiv preprint arXiv:2511.21772 , year =.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models arXiv preprint arXiv:2511.21772 , year =

Reference 5

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Observation 9590a9a0-da35-49a6-af75-364e296a19f5 · outbound

This paper cites Cp-clip: Core- periphery feature alignment clip for zero-shot medical image analysis,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Cp-clip: Core- periphery feature alignment clip for zero-shot medical image analysis,

Reference 6

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Observation a96f39f0-b6a7-4821-b4f4-41845e6e0367 · outbound

This paper cites Vpl: Visual proxy learning framework for zero-shot medical image diagnosis,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Vpl: Visual proxy learning framework for zero-shot medical image diagnosis,

Reference 7

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Observation 69da6986-dd80-449f-b039-214e0afdb685 · outbound

This paper cites Medclip: Contrastive learning from unpaired medical images and text,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Medclip: Contrastive learning from unpaired medical images and text,

Reference 8

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Observation ea79b72a-9672-4306-a769-6e3b63c12818 · outbound

This paper cites Pros: Prompting-to-simulate generalized knowledge for universal cross-domain retrieval,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Pros: Prompting-to-simulate generalized knowledge for universal cross-domain retrieval,

Reference 9

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Observation 969637b0-7bf3-4095-ba78-55a833cff838 · outbound

This paper cites Depro: Domain ensemble using decoupled prompts for universal cross-domain retrieval,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Depro: Domain ensemble using decoupled prompts for universal cross-domain retrieval,

Reference 10

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Observation cf5c23bd-f9bd-449a-aa6d-c135d760bb0e · outbound

This paper cites Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation

Reference 11

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Observation 0e4813c8-df96-4405-b3ce-000cb89a74f5 · outbound

This paper cites Building a multi-modal spatiotemporal expert for zero-shot action recognition with clip,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Building a multi-modal spatiotemporal expert for zero-shot action recognition with clip,

Reference 12

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Observation b2e48ed2-d29a-488c-98ff-cea4fcbaf4a5 · outbound

This paper cites Leveraging temporal contextu- alization for video action recognition,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Leveraging temporal contextu- alization for video action recognition,

Reference 13

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Observation 5c421381-d968-4398-83b6-1f90b8ada3c1 · outbound

This paper cites Open-vclip: Transforming clip to an open-vocabulary video model via interpolated weight optimization,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Open-vclip: Transforming clip to an open-vocabulary video model via interpolated weight optimization,

Reference 14

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Observation 7fa52433-aac7-47e0-bcf1-ab042fc13da8 · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Learning to prompt for vision- language models,

Reference 16

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Observation 776412d6-ced3-4dfa-bd60-f038ea57b9ec · outbound

This paper cites Conditional prompt learning for vision-language models,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Conditional prompt learning for vision-language models,

Reference 17

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Observation fbd06bec-ab04-4f22-931e-382546746f42 · outbound

This paper cites Visual-language prompt tuning with knowledge-guided context optimization,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Visual-language prompt tuning with knowledge-guided context optimization,

Reference 18

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Observation 667653dc-a60b-4db9-8c62-f584b430ad86 · outbound

This paper cites Self-regulating prompts: Foundational model adaptation without forgetting,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Self-regulating prompts: Foundational model adaptation without forgetting,

Reference 19

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Observation 9cf2aba9-ac7e-4592-8a79-764c4ed1aae8 · outbound

This paper cites Maple: Multi-modal prompt learning,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Maple: Multi-modal prompt learning,

Reference 20

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Observation d6c56974-9c0d-4f0a-a209-2f77b7676fd6 · outbound

This paper cites Mmrl: Multi-modal representation learning for vision-language models,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Mmrl: Multi-modal representation learning for vision-language models,

Reference 21

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Observation b45e2041-2883-462b-ba96-11665a76d92b · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 22

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Observation a4e49347-c519-4420-bec0-5335364eef30 · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Tip-adapter: Training-free adaption of clip for few-shot classification,

Reference 23

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Observation d8e48f86-a9f7-4bab-9625-855813514b7f · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Clip-adapter: Better vision-language models with feature adapters,

Reference 24

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Observation 7be2556d-ed72-4e07-b8c2-428effb1ffd5 · outbound

This paper cites Mma: Multi-modal adapter for vision-language models,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Mma: Multi-modal adapter for vision-language models,

Reference 25

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Observation 143b27d9-b305-4486-8d0d-9395b9317ca3 · outbound

This paper cites Scaling & shifting your features: A new baseline for efficient model tuning,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Scaling & shifting your features: A new baseline for efficient model tuning,

Reference 26

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Observation 9500fe27-4700-422b-9ffb-6395f60786c7 · outbound

This paper cites Multi-modal interactive agent layer for few-shot universal cross-domain retrieval and beyond,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Multi-modal interactive agent layer for few-shot universal cross-domain retrieval and beyond,

Reference 27

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Observation 2bdd98b8-33db-4113-8150-bc6d4bc125db · outbound

This paper cites Vilt: Vision-and-language transformer without convolution or region supervision,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Vilt: Vision-and-language transformer without convolution or region supervision,

Reference 28

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Observation bee89f22-6100-4c4f-a9e3-8c750374d758 · outbound

This paper cites Align before fuse: Vision and language representation learning with momentum distillation,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Align before fuse: Vision and language representation learning with momentum distillation,

Reference 29

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Observation 3fc24b0a-1085-4894-b559-21291b11214b · outbound

This paper cites Vlmo: Unified vision-language pre- 12 training with mixture-of-modality-experts,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Vlmo: Unified vision-language pre- 12 training with mixture-of-modality-experts,

Reference 30

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Observation d8ef49b5-f256-458c-803a-e1f016c51fc7 · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 31

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Observation 918bb84d-aa35-4c01-ac7c-2490ad2e7b3b · outbound

This paper cites Visual instruction tuning,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Visual instruction tuning,

Reference 32

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Observation ce677725-b034-4345-b9c7-fa2419d11132 · outbound

This paper cites Parameter-efficient transfer learning for nlp,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Parameter-efficient transfer learning for nlp,

Reference 33

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Observation d71f8aa0-7ab6-4d4b-8fee-e2c6b9fdbea2 · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Lora: Low-rank adaptation of large language models,

Reference 34

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Observation 425f9359-f853-4436-82e9-33a02a482345 · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Bitfit: Simple parameter- efficient fine-tuning for transformer-based masked language-models,

Reference 35

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Observation 1b473cbc-a176-46bb-a819-62e483f4938d · outbound

This paper cites Learning with enriched inductive biases for vision-language models,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Learning with enriched inductive biases for vision-language models,

Reference 36

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Observation e8df2e91-4aa0-4156-bdcb-9d7bdfbdf19a · outbound

This paper cites Not all features matter: Enhancing few-shot CLIP with adaptive prior refinement,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Not all features matter: Enhancing few-shot CLIP with adaptive prior refinement,

Reference 37

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Observation 89facad9-e895-4d0b-8d3e-bf529d32b59d · outbound

This paper cites Task residual for tuning vision-language models,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Task residual for tuning vision-language models,

Reference 38

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Observation f8f16d64-8ac6-4804-8dd3-e1fc83ecf811 · outbound

This paper cites Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners,

Reference 39

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Observation 4aeb8e29-5cfe-420a-8794-f1ae7b2e4fb3 · outbound

This paper cites Attention is all you need,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Attention is all you need,

Reference 40

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Observation 6e726c4f-339a-4614-92ee-5bbd4926af4f · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Imagenet: A large-scale hierarchical image database,

Reference 41

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Observation 97044348-37d3-4c02-b202-f0184dc999ab · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,

Reference 42

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Observation 2dbd24bb-d811-4cde-9136-1ca21837bd01 · outbound

This paper cites Cats and dogs,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Cats and dogs,

Reference 43

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Observation 6b61579f-c24b-4ac7-8b97-960bf17f1d20 · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models 3d object representations for fine-grained categorization,

Reference 44

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Observation 297235c9-f9ee-498f-892d-e6222d7d72e9 · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Automated flower classification over a large number of classes,

Reference 45

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Observation 68427a17-e17e-4f05-9d07-2fa07e4aa935 · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Food-101–mining discriminative components with random forests,

Reference 46

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Observation a36021cc-78b3-4fbd-b227-1ff06103faa5 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Fine-Grained Visual Classification of Aircraft

Reference 47

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Observation 0e307de1-8908-43ea-bb42-6567c367a4a4 · outbound

This paper cites Sun database: Large-scale scene recognition from abbey to zoo,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Sun database: Large-scale scene recognition from abbey to zoo,

Reference 48

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Observation 3fd93f93-afd6-4296-9950-8474a5971ef4 · outbound

This paper cites A dataset of 101 human action classes from videos in the wild,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models A dataset of 101 human action classes from videos in the wild,

Reference 49

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Observation 67c31fa7-9e9a-4a62-a735-ebe3044f8d7b · outbound

This paper cites Describing textures in the wild,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Describing textures in the wild,

Reference 50

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Observation 63b3f018-d004-4c2e-8f60-5f2c907040d9 · outbound

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

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,

Reference 51

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Observation 7231f43e-3f34-44f2-9c5c-2ec0440fc988 · outbound

This paper cites Do imagenet classifiers generalize to imagenet?.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Do imagenet classifiers generalize to imagenet?

Reference 52

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Observation ed4039c3-9bc3-4b15-a7c0-04526ebec405 · outbound

This paper cites Learning robust global representations by penalizing local predictive power,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Learning robust global representations by penalizing local predictive power,

Reference 53

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Observation 1f3abb92-4ba5-4039-8d86-196336e1b920 · outbound

This paper cites Natural adversarial examples,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Natural adversarial examples,

Reference 54

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Observation 5f0708fb-a9dd-465e-adc4-ce6e83aa0c4d · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models The many faces of robustness: A critical analysis of out-of-distribution generalization,

Reference 55

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Observation 77935810-de96-4a9a-af3c-9c8fbe8bfd9a · outbound

This paper cites Moment matching for multi-source domain adaptation,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Moment matching for multi-source domain adaptation,

Reference 56

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Observation 3b884223-0559-4206-a4f4-da0dbef047a6 · outbound

This paper cites The sketchy database: learning to retrieve badly drawn bunnies,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models The sketchy database: learning to retrieve badly drawn bunnies,

Reference 57

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Observation 98fca510-8bfe-41c0-9329-0a04adad9a81 · outbound

This paper cites Deep sketch hashing: Fast free-hand sketch-based image retrieval,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Deep sketch hashing: Fast free-hand sketch-based image retrieval,

Reference 58

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Observation 61a97fd1-cb6b-4d14-958c-851981d987f6 · outbound

This paper cites How do humans sketch objects?.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models How do humans sketch objects?

Reference 59

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Observation a5e52f92-a3d4-42b9-a688-3db858ae88e9 · outbound

This paper cites Sketchnet: Sketch classification with web images,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Sketchnet: Sketch classification with web images,

Reference 60

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MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Tcp: Textual-based class-aware prompt tuning for visual-language model,

Reference 61

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This paper cites Divergence-enhanced knowledge-guided context optimization for visual-language prompt tun- ing,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Divergence-enhanced knowledge-guided context optimization for visual-language prompt tun- ing,

Reference 62

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MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Bi-modality individual- aware prompt tuning for visual-language model,

Reference 63

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MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Frequency-based comprehensive prompt learning for vision-language models,

Reference 64

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MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Hierarchical cross- modal prompt learning for vision-language models,

Reference 65

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MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Promptkd: Unsupervised prompt distillation for vision-language mod- els,

Reference 66

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This paper cites Adapt- former: Adapting vision transformers for scalable visual recognition,.

MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Adapt- former: Adapting vision transformers for scalable visual recognition,

Reference 67

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MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models Visual prompt tuning,

Reference 68

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