Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model | Paper Citation Record

Observation e86f5ceb-6d13-4cb1-9b96-99e66cbdf918 · outbound

This paper cites Improved baselines with visual instruction tuning,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Improved baselines with visual instruction tuning,

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Observation 897f254f-9060-4897-ad67-134a5d0204f3 · outbound

This paper cites Visual instruction tuning,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Visual instruction tuning,

Reference 2

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Observation 15320d7f-2bef-416e-8d67-50b98ea64558 · outbound

This paper cites MiniGPT-4: Enhancing vision-language understanding with advanced large language models,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model MiniGPT-4: Enhancing vision-language understanding with advanced large language models,

Reference 3

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Observation 3eddf7f0-7c4b-4664-9b82-e80b160f1bd0 · outbound

This paper cites VisualGPT: Data- efficient adaptation of pretrained language models for image captioning,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model VisualGPT: Data- efficient adaptation of pretrained language models for image captioning,

Reference 4

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Observation 90d9ecca-f7a3-4bd2-9e78-0c91846239c2 · outbound

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

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 5

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Observation a1a3d2eb-2bed-4b1b-be52-73b7730b9fe2 · outbound

This paper cites Adapting multimodal large language models for video question answering by capturing question-critical and coherent moments,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Adapting multimodal large language models for video question answering by capturing question-critical and coherent moments,

Reference 6

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Observation 2a06d98d-0fda-4d04-9756-cd6cc847735b · outbound

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

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Learning transferable visual models from natural language supervision,

Reference 7

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Observation 12f40afe-1631-4417-88a1-f3c86fb2b1de · outbound

This paper cites MoE-LLaV A: Mixture of experts for large vision- language models,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model MoE-LLaV A: Mixture of experts for large vision- language models,

Reference 8

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Observation cdd7ed22-bb76-4011-8970-3fa67b92f0e3 · outbound

This paper cites Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality,

Reference 9

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Observation 079c2e7c-ef68-4a16-815e-4989f1412047 · outbound

This paper cites On evaluating adversarial robustness of large vision-language models,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model On evaluating adversarial robustness of large vision-language models,

Reference 10

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Observation 164b444f-0f0e-4263-b9c4-37456b8f61f3 · outbound

This paper cites On the adversarial robustness of multi- modal foundation models,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model On the adversarial robustness of multi- modal foundation models,

Reference 11

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Observation 785c7e2a-7414-4a7a-ac3a-a8273b4dc8c5 · outbound

This paper cites Are aligned neural networks adversarially aligned?.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Are aligned neural networks adversarially aligned?

Reference 12

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Observation 72348382-e4f1-4b55-a73a-4db267e12b10 · outbound

This paper cites How Robust is Google's Bard to Adversarial Image Attacks?.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model How Robust is Google's Bard to Adversarial Image Attacks?

Reference 13

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Observation 4bd9fb85-c43b-47ea-9daa-c1afa33c5326 · outbound

This paper cites Towards evaluating the robustness of neural networks,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Towards evaluating the robustness of neural networks,

Reference 14

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Observation ffa632ac-88ec-48a0-93df-d93c3be202cf · outbound

This paper cites Explaining and harnessing adversarial examples,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Explaining and harnessing adversarial examples,

Reference 15

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This paper cites Towards deep learning models resistant to adversarial attacks,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Towards deep learning models resistant to adversarial attacks,

Reference 16

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Observation 75a916ce-a1d8-4b91-a118-c19cb3c23b8b · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 17

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This paper cites Understanding zero-shot adversarial robustness for large-scale models,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Understanding zero-shot adversarial robustness for large-scale models,

Reference 18

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Observation 4328eecf-ad24-4a47-b5c9-3fdf1193fda9 · outbound

This paper cites Robust CLIP: Unsupervised adversarial fine-tuning of vision embeddings for robust large vision-language models,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Robust CLIP: Unsupervised adversarial fine-tuning of vision embeddings for robust large vision-language models,

Reference 19

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Observation 6cd7e1b4-bb88-46c3-a360-6608dc9e870c · outbound

This paper cites Bag of tricks for adversarial training,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Bag of tricks for adversarial training,

Reference 20

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Observation b174008f-4cb3-4ac9-b7bb-5d2ac8f574f9 · outbound

This paper cites Adversarial training for free!.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Adversarial training for free!

Reference 21

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This paper cites Theoretically principled trade-off between robustness and accuracy,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Theoretically principled trade-off between robustness and accuracy,

Reference 22

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Observation 5bdd8266-a573-4b17-9166-46a704cc9ccd · outbound

This paper cites Pre-trained model guided fine-tuning for zero-shot adversarial robustness,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Pre-trained model guided fine-tuning for zero-shot adversarial robustness,

Reference 23

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Observation 49de1d72-1176-486f-89e8-100d5f5e0885 · outbound

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

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model ImageNet: A large-scale hierarchical image database,

Reference 24

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Observation 32cd3904-0de6-4cbe-8e6f-c6f71b99ab99 · outbound

This paper cites Towards adversarial attack on vision- language pre-training models,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Towards adversarial attack on vision- language pre-training models,

Reference 25

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Observation ccb3e61f-3fc7-46fd-9aa7-636a73e0981e · outbound

This paper cites Exploring transferability of multimodal adversarial samples for vision-language pre-training models with contrastive learning,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Exploring transferability of multimodal adversarial samples for vision-language pre-training models with contrastive learning,

Reference 26

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Observation b6da57d6-9435-4598-a48d-6f2cdc9e8780 · outbound

This paper cites Enhancing descriptive captions with visual attributes for multimodal perception,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Enhancing descriptive captions with visual attributes for multimodal perception,

Reference 27

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Observation 0aa53f28-0843-4a2b-81ea-09f6898e4d00 · outbound

This paper cites Prefix conditioning unifies language and label supervision,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Prefix conditioning unifies language and label supervision,

Reference 28

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Observation 9be87bc1-f9ba-4a73-9ec2-be007d6aa00c · outbound

This paper cites Quality text, robust vision: The role of language in enhancing visual robustness of vision-language models,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Quality text, robust vision: The role of language in enhancing visual robustness of vision-language models,

Reference 29

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This paper cites BLIP-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model BLIP-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 30

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This paper cites Otter: A multi-modal model with in-context instruction tuning,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Otter: A multi-modal model with in-context instruction tuning,

Reference 31

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This paper cites InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition

Reference 32

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This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Gemini: A Family of Highly Capable Multimodal Models

Reference 33

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This paper cites GPT-4 Technical Report.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model GPT-4 Technical Report

Reference 34

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This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,

Reference 35

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This paper cites Boosting adversarial attacks with momentum,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Boosting adversarial attacks with momentum,

Reference 36

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This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,

Reference 37

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This paper cites Adversarial weight perturbation helps robust generalization,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Adversarial weight perturbation helps robust generalization,

Reference 38

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This paper cites Adversarial Attacks on Foundational Vision Models.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Adversarial Attacks on Foundational Vision Models

Reference 39

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This paper cites Set-level guidance attack: Boosting adversarial transferability of vision-language pre-training models,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Set-level guidance attack: Boosting adversarial transferability of vision-language pre-training models,

Reference 40

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This paper cites Revisiting the ad- versarial transferability:towards a perspective of semantic preservation,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Revisiting the ad- versarial transferability:towards a perspective of semantic preservation,

Reference 41

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This paper cites Microsoft COCO: Common objects in context,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Microsoft COCO: Common objects in context,

Reference 42

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This paper cites Caltech-256 object category dataset,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Caltech-256 object category dataset,

Reference 43

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

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model 3D object representations for fine-grained categorization,

Reference 44

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

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Learning multiple layers of features from tiny images,

Reference 45

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This paper cites Describing textures in the wild,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Describing textures in the wild,

Reference 46

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

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classification,

Reference 47

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This paper cites Fine-Grained Visual Classification of Aircraft.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Fine-Grained Visual Classification of Aircraft

Reference 48

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

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Automated flower classification over a large number of classes,

Reference 49

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Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Cats and dogs,

Reference 50

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This paper cites An analysis of single-layer networks in unsupervised feature learning,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model An analysis of single-layer networks in unsupervised feature learning,

Reference 51

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

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model The many faces of robustness: A critical analysis of out-of-distribution generalization,

Reference 52

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This paper cites Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,

Reference 53

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This paper cites Making the V in VQA matter: Elevating the role of image understanding in visual question answering,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Making the V in VQA matter: Elevating the role of image understanding in visual question answering,

Reference 54

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This paper cites Towards VQA models that can read,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Towards VQA models that can read,

Reference 55

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This paper cites VizWiz grand challenge: Answering visual questions from blind people,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model VizWiz grand challenge: Answering visual questions from blind people,

Reference 56

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This paper cites OK-VQA: A visual question answering benchmark requiring external knowledge,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model OK-VQA: A visual question answering benchmark requiring external knowledge,

Reference 57

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This paper cites CIDEr: Consensus-based image description evaluation,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model CIDEr: Consensus-based image description evaluation,

Reference 58

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This paper cites LAION-5B: An open large-scale dataset for training next generation image-text models,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model LAION-5B: An open large-scale dataset for training next generation image-text models,

Reference 59

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This paper cites Decoupled weight decay regularization,.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model Decoupled weight decay regularization,

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This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

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