When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness | Paper Citation Record

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This paper cites Combating adver- saries with anti-adversaries.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Combating adver- saries with anti-adversaries

Reference 1

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This paper cites Square attack: a query-efficient black-box adversarial attack via random search.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Square attack: a query-efficient black-box adversarial attack via random search

Reference 2

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This paper cites Obfus- cated gradients give a false sense of security: Circumventing defenses to adversarial examples.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Obfus- cated gradients give a false sense of security: Circumventing defenses to adversarial examples

Reference 3

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Synthesizing robust adversarial examples

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This paper cites Recent advances in adversarial training for adversarial ro- bustness.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Recent advances in adversarial training for adversarial ro- bustness

Reference 5

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This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 6

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Food-101–mining discriminative components with random forests

Reference 7

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This paper cites Towards evaluating the robustness of neural networks.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Towards evaluating the robustness of neural networks

Reference 8

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Observation e6ba3083-1249-4ade-92bc-0ca6d56f1df9 · outbound

This paper cites Multi-cache enhanced prototype learning for test- time generalization of vision-language models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Multi-cache enhanced prototype learning for test- time generalization of vision-language models

Reference 9

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This paper cites Describing textures in the wild.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Describing textures in the wild

Reference 10

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

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness An analysis of single-layer networks in unsupervised feature learning

Reference 11

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Observation 93a8f72c-ed29-4f89-834f-2de884fce3f6 · outbound

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Certified adversarial robustness via randomized smoothing

Reference 12

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Observation 1840c40c-a5e7-4726-8508-a9fbee8c8449 · outbound

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

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 13

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Observation 26cd9629-7771-4a3e-9fa7-8d97e7ffdf1e · outbound

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

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Imagenet: A large-scale hierarchical image database

Reference 14

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This paper cites Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 15

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This paper cites Explaining and harnessing adversarial examples.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Explaining and harnessing adversarial examples

Reference 16

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This paper cites Caltech- 256 object category dataset.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Caltech- 256 object category dataset

Reference 17

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This paper cites an unresolved cited work.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Unresolved cited work

Reference 18

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This paper cites Augmix: A simple data processing method to improve robustness and uncertainty.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Augmix: A simple data processing method to improve robustness and uncertainty

Reference 19

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Observation 2e48862a-dff7-4842-b1d3-e954847be5e3 · outbound

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

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness The many faces of robust- ness: A critical analysis of out-of-distribution generalization

Reference 20

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Observation 891e51e2-4cd9-4b3b-9a1e-de43226417c5 · outbound

This paper cites Natural adversarial examples.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Natural adversarial examples

Reference 21

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This paper cites Cosmic: Clique- oriented semantic multi-space integration for robust clip test- time adaptation.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Cosmic: Clique- oriented semantic multi-space integration for robust clip test- time adaptation

Reference 22

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This paper cites Black-box adversarial attacks with limited queries and information.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Black-box adversarial attacks with limited queries and information

Reference 23

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This paper cites Test-time prompt tuning for zero-shot depth comple- tion.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Test-time prompt tuning for zero-shot depth comple- tion

Reference 24

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

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Scaling up visual and vision-language representation learning with noisy text supervision

Reference 25

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Observation 31a39e4e-07e0-4878-8b85-f39fd5549e9a · outbound

This paper cites Efficient test-time adaptation of vision-language models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Efficient test-time adaptation of vision-language models

Reference 26

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Observation a7d65593-0508-4651-9476-d791a8c8579d · outbound

This paper cites Swag-net: Semantic word-aware graph network for temporal video grounding.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Swag-net: Semantic word-aware graph network for temporal video grounding

Reference 27

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This paper cites Gaussian mixture proposals with pull- push learning scheme to capture diverse events for weakly supervised temporal video grounding.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Gaussian mixture proposals with pull- push learning scheme to capture diverse events for weakly supervised temporal video grounding

Reference 28

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Observation 745aa5cc-9a96-444b-a4d5-3b4ae523b134 · outbound

This paper cites Learnable negative proposals using dual-signed cross- entropy loss for weakly supervised video moment localiza- tion.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Learnable negative proposals using dual-signed cross- entropy loss for weakly supervised video moment localiza- tion

Reference 29

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This paper cites Finding opti- mal video moment without training: Gaussian boundary op- timization for weakly supervised video grounding.IEEE Transactions on Multimedia, 2026.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Finding opti- mal video moment without training: Gaussian boundary op- timization for weakly supervised video grounding.IEEE Transactions on Multimedia, 2026

Reference 30

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness 3d object representations for fine-grained categorization

Reference 31

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

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Learning multiple layers of features from tiny images

Reference 32

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This paper cites Ad- versarial examples in the physical world.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Ad- versarial examples in the physical world

Reference 33

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This paper cites One prompt word is enough to boost adversarial robustness for pre-trained vision-language models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness One prompt word is enough to boost adversarial robustness for pre-trained vision-language models

Reference 34

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Observation 097707a5-f5ea-45b4-957b-d2acfb448a9d · outbound

This paper cites Language-driven anchors for zero-shot ad- versarial robustness.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Language-driven anchors for zero-shot ad- versarial robustness

Reference 35

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Visual instruction tuning

Reference 36

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Visualiz- ing data using t-sne.Journal of machine learning research,

Reference 37

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Towards deep learning models resistant to adversarial attacks

Reference 38

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Fine-Grained Visual Classification of Aircraft

Reference 39

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Understanding zero-shot adversarial robust- ness for large-scale models

Reference 40

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This paper cites Deepfool: a simple and accurate method to fool deep neural networks.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Deepfool: a simple and accurate method to fool deep neural networks

Reference 41

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Universal adversarial perturba- tions

Reference 42

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Diffusion models for ad- versarial purification

Reference 43

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

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Automated flower classification over a large number of classes

Reference 44

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness The limitations of deep learning in adversarial settings

Reference 45

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Cats and dogs

Reference 46

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Enhancing adversarial robustness via test-time transforma- tion ensembling

Reference 47

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This paper cites Learn- ing transferable visual models from natural language super- vision.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Learn- ing transferable visual models from natural language super- vision

Reference 48

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Zero-shot text-to-image generation

Reference 49

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This paper cites Do imagenet classifiers generalize to im- agenet? InICML, 2019.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Do imagenet classifiers generalize to im- agenet? InICML, 2019

Reference 50

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When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Overfitting in ad- versarially robust deep learning

Reference 51

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This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Photorealistic text-to-image diffusion models with deep language understanding

Reference 52

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This paper cites Provably robust deep learning via adversarially trained smoothed clas- sifiers.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Provably robust deep learning via adversarially trained smoothed clas- sifiers

Reference 53

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Observation 0cf6b6cc-082d-4554-a587-d0086feda409 · outbound

This paper cites Denoised smoothing: A provable defense for pretrained classifiers.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Denoised smoothing: A provable defense for pretrained classifiers

Reference 54

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

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Observation 7676b41b-9138-428b-9697-4ea867622902 · outbound

This paper cites Defense-gan: Protecting classifiers against adversarial at- tacks using generative models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Defense-gan: Protecting classifiers against adversarial at- tacks using generative models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 5da7503b-424a-416a-8c9e-3ddcc5b3dbd9 · outbound

This paper cites Robust clip: Unsupervised ad- versarial fine-tuning of vision embeddings for robust large vision-language models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Robust clip: Unsupervised ad- versarial fine-tuning of vision embeddings for robust large vision-language models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 96879c3c-e5a7-4864-b349-095da9885761 · outbound

This paper cites Improving robustness against common corruptions by covariate shift adaptation.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Improving robustness against common corruptions by covariate shift adaptation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 192ead39-5c0a-412c-944e-b823ffe3d251 · outbound

This paper cites Adversarial training for free! InNeurIPS, 2019.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Adversarial training for free! InNeurIPS, 2019

Reference 58

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

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Observation aa46e930-0050-4eec-8763-8135e65198bd · outbound

This paper cites O-tpt: Orthogonality constraints for calibrating test-time prompt tuning in vision-language models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness O-tpt: Orthogonality constraints for calibrating test-time prompt tuning in vision-language models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

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Observation b0189979-1435-48ad-8c70-a5bf5f6a92ab · outbound

This paper cites Jail- break in pieces: Compositional adversarial attacks on multi- modal language models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Jail- break in pieces: Compositional adversarial attacks on multi- modal language models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

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Observation 7562e373-cbb5-4c3b-a54e-f889ee75eb90 · outbound

This paper cites R-tpt: Improving adversarial robustness of vision-language models through test-time prompt tuning.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness R-tpt: Improving adversarial robustness of vision-language models through test-time prompt tuning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fec00b0f-b352-4c11-95d7-e773618aa7c5 · outbound

This paper cites Test- time prompt tuning for zero-shot generalization in vision- language models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Test- time prompt tuning for zero-shot generalization in vision- language models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation f2a5226f-32e5-49e1-81e0-d66fb00bb3c3 · outbound

This paper cites Clip models are few-shot learners: Empirical studies on vqa and visual entailment.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Clip models are few-shot learners: Empirical studies on vqa and visual entailment

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation f6074b01-5520-4059-b593-e2b7284f19fd · outbound

This paper cites A dataset of 101 human action classes from videos in the wild.Center for Research in Computer Vision, 2012.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness A dataset of 101 human action classes from videos in the wild.Center for Research in Computer Vision, 2012

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

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Observation 23e1ba3a-4a6d-4630-9dd6-fca1666e0254 · outbound

This paper cites One pixel attack for fooling deep neural networks.IEEE Transactions on Evolutionary Computation, 2019.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness One pixel attack for fooling deep neural networks.IEEE Transactions on Evolutionary Computation, 2019

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 5b3ac568-e2f3-4938-aa8f-d25bcd4135dc · outbound

This paper cites Test-time training with self- supervision for generalization under distribution shifts.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Test-time training with self- supervision for generalization under distribution shifts

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 9408dd0b-5a47-4881-bace-c31207207cef · outbound

This paper cites In- triguing properties of neural networks.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness In- triguing properties of neural networks

Reference 67

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation e0a495a4-e047-45cb-9661-aeeab4da3645 · outbound

This paper cites Tesla: Test-time self-learning with au- tomatic adversarial augmentation.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Tesla: Test-time self-learning with au- tomatic adversarial augmentation

Reference 68

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 5705fcf2-6209-41b6-949d-0d0aee1ee5e4 · outbound

This paper cites On the zero-shot adversarial robustness of vision-language models: A truly zero-shot and training-free approach.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness On the zero-shot adversarial robustness of vision-language models: A truly zero-shot and training-free approach

Reference 69

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee8e3c6b-dcb1-430b-b24d-5ee73bcb9f07 · outbound

This paper cites Tent: Fully test-time adaptation by entropy minimization.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Tent: Fully test-time adaptation by entropy minimization

Reference 70

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation d7fc2572-bfdb-45f7-bda8-300a0c8167e4 · outbound

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

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Learning robust global representations by penalizing local predictive power

Reference 71

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation e5cca145-03a1-4be3-afb5-acee32ff0caa · outbound

This paper cites Con- tinual test-time domain adaptation.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Con- tinual test-time domain adaptation

Reference 72

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation d975bdfb-d6d9-40ea-a80b-3731e7331493 · outbound

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

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Pre- trained model guided fine-tuning for zero-shot adversarial robustness

Reference 73

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation cd7bc9d8-d19c-4987-a17d-5f9a90ae3ee3 · outbound

This paper cites Tapt: Test-time adversarial prompt tuning for robust inference in vision-language models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Tapt: Test-time adversarial prompt tuning for robust inference in vision-language models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 6df9f27e-dee0-4fd7-a116-c6e98654ad89 · outbound

This paper cites Fast is better than free: Revisiting adversarial training.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Fast is better than free: Revisiting adversarial training

Reference 75

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

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Observation 557b0c9d-2c97-45dc-a90e-25ff4097f3de · outbound

This paper cites Attacking Adversarial Attacks as A Defense.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Attacking Adversarial Attacks as A Defense

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:37:09.665160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a6906326-8d1f-4013-aaf5-c5b496119235 · outbound

This paper cites Adversarial weight perturbation helps robust generalization.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Adversarial weight perturbation helps robust generalization

Reference 77

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 3147a9a2-9458-45f1-b6f6-5e06e81e4d45 · outbound

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

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Sun database: Large-scale scene recognition from abbey to zoo

Reference 78

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 0ae7fcc4-18f0-4448-8f2a-9caebe4b57f3 · outbound

This paper cites Improving transferabil- 8 ity of adversarial examples with input diversity.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Improving transferabil- 8 ity of adversarial examples with input diversity

Reference 79

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 3e03ef9e-9ec7-4245-853b-0bfebb50e920 · outbound

This paper cites Chain of attack: On the robustness of vision-language models against transfer-based adversarial attacks.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Chain of attack: On the robustness of vision-language models against transfer-based adversarial attacks

Reference 80

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation b25dc8fd-98ea-4f49-8dcd-b187aa34a257 · outbound

This paper cites Clip is strong enough to fight back: Test-time counterattacks to- wards zero-shot adversarial robustness of clip.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Clip is strong enough to fight back: Test-time counterattacks to- wards zero-shot adversarial robustness of clip

Reference 81

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation aa0075ed-5905-4a2b-80c7-0895a885773a · outbound

This paper cites Detclipv2: Scal- able open-vocabulary object detection pre-training via word- region alignment.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Detclipv2: Scal- able open-vocabulary object detection pre-training via word- region alignment

Reference 82

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation a72feee5-dcd7-4679-9ff0-d9b6900b4666 · outbound

This paper cites Adversarial purification with score-based generative models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Adversarial purification with score-based generative models

Reference 83

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 0e519c24-9d7e-42e5-86c4-43a6d569adc8 · outbound

This paper cites Coca: Contrastive captioners are image-text foundation models.Transactions on Machine Learning Research, 2022.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Coca: Contrastive captioners are image-text foundation models.Transactions on Machine Learning Research, 2022

Reference 84

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 0c7db467-1d87-419b-bd43-5d592c60e3b2 · outbound

This paper cites On the test-time zero- shot generalization of vision-language models: Do we really need prompt learning? InCVPR, 2024.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness On the test-time zero- shot generalization of vision-language models: Do we really need prompt learning? InCVPR, 2024

Reference 85

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

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Observation 8afa9940-36a2-4bc4-8b96-57f262376156 · outbound

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

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Theoretically principled trade-off between robustness and accuracy

Reference 86

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

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Observation 8b397f23-3bd2-49ea-8feb-d33eaa8a999a · outbound

This paper cites Vision-language models for vision tasks: A survey.IEEE transactions on pattern analysis and machine intelligence,.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Vision-language models for vision tasks: A survey.IEEE transactions on pattern analysis and machine intelligence,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

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Observation fc265349-8aaa-4e8a-a3a1-42dffc368fc0 · outbound

This paper cites Adversarial prompt tuning for vision-language models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Adversarial prompt tuning for vision-language models

Reference 88

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 299e0b61-2483-4276-a8bc-4c73e2a9ca26 · outbound

This paper cites Tip-adapter: Training-free clip-adapter for better vision- language modeling.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Tip-adapter: Training-free clip-adapter for better vision- language modeling

Reference 89

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation ed088b67-f7cf-4386-a2a5-af1f87c4a7b8 · outbound

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

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness On evaluating adversarial robustness of large vision-language models

Reference 90

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c804a5f0-d14a-4510-8b30-35f9b5e2da23 · outbound

This paper cites Regionclip: Region-based language-image pretraining.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Regionclip: Region-based language-image pretraining

Reference 91

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation b34f5620-ff31-4fbd-b187-ff9310b86eba · outbound

This paper cites Bayesian test-time adaptation for vision-language models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Bayesian test-time adaptation for vision-language models

Reference 92

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

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Observation 50879b66-0553-49b7-a21b-8417df33b541 · outbound

This paper cites Few-shot adversarial prompt learning on vision-language models.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Few-shot adversarial prompt learning on vision-language models

Reference 93

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68803f70-265b-4f14-9190-03ea39562f97 · outbound

This paper cites Enhancing clip robustness via cross-modality alignment.

When CLIP Sees More, It Fights Back Harder: Multi-View Guided Adaptive Counterattacks for Test-Time Adversarial Robustness Enhancing clip robustness via cross-modality alignment

Reference 94

Resolution: unresolved; no resolver link, observed 2026-06-27T22:27:55.156434Z

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