Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI | Paper Citation Record

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This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 1

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This paper cites Representation learning and na- ture encoded fusion for heterogeneous sensor networks,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Representation learning and na- ture encoded fusion for heterogeneous sensor networks,

Reference 2

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This paper cites Congestion aware dynamic user association in heterogeneous cellular network: A stochastic decision approach,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Congestion aware dynamic user association in heterogeneous cellular network: A stochastic decision approach,

Reference 3

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This paper cites Enhanced robustness by symmetry enforcement,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Enhanced robustness by symmetry enforcement,

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This paper cites Partial interference alignment for heterogeneous cellular networks,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Partial interference alignment for heterogeneous cellular networks,

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Observation d61ebdc0-a3e0-4c47-9539-b20da33af6c3 · outbound

This paper cites Optimization for user centric massive mimo cell free networks via large system analysis,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Optimization for user centric massive mimo cell free networks via large system analysis,

Reference 6

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This paper cites Exploration vs exploitation for distributed channel access in cognitive radio networks: A multi-user case study,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Exploration vs exploitation for distributed channel access in cognitive radio networks: A multi-user case study,

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This paper cites Deep reinforcement learning based computation offloading for mobility-aware edge computing,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Deep reinforcement learning based computation offloading for mobility-aware edge computing,

Reference 9

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Observation e4dffcba-d670-4690-a55a-66053719d781 · outbound

This paper cites Performance analysis of co- operative multicell precoding with global csi and local individual csi in the large dimensional regime,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Performance analysis of co- operative multicell precoding with global csi and local individual csi in the large dimensional regime,

Reference 10

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This paper cites Low complexity optimization for user centric cel- lular networks via large dimensional analysis,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Low complexity optimization for user centric cel- lular networks via large dimensional analysis,

Reference 11

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Observation 080326ea-9f1c-40ed-a3a6-f66ce1a212ab · outbound

This paper cites Improving robustness of deep neural networks via large-difference transformation,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Improving robustness of deep neural networks via large-difference transformation,

Reference 12

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This paper cites Looking beyond content: Modeling and detection of fake news from a social context perspective.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Looking beyond content: Modeling and detection of fake news from a social context perspective

Reference 13

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Observation b70425f5-9a7f-40fa-a5f3-fc1c4fc27c6d · outbound

This paper cites Large system analysis for densification of cellular networks with massive mimo,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Large system analysis for densification of cellular networks with massive mimo,

Reference 14

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Observation a73bec4b-fc33-4bd0-ae8e-aa38cba8ae2e · outbound

This paper cites Collabora- tive spectrum sharing based on information pooling for cognitive radio networks with channel heterogeneity,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Collabora- tive spectrum sharing based on information pooling for cognitive radio networks with channel heterogeneity,

Reference 15

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Observation 4187ea7e-2c05-4ca1-aa10-17fbc28ccd8d · outbound

This paper cites Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness

Reference 16

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Observation 8a2fd787-2e48-4a4b-9cd7-e89decb85427 · outbound

This paper cites Information theory and represen- tation learning inspired multimodal data fusion,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Information theory and represen- tation learning inspired multimodal data fusion,

Reference 17

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Observation 560e8a2e-44dc-4470-a3d9-6adbcdc5c858 · outbound

This paper cites Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning

Reference 19

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Observation 5bf5c5cb-b07f-465f-b50e-d916ce2e0b23 · outbound

This paper cites Multi-scale unrectified push-pull with channel attention for enhanced corruption robustness,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Multi-scale unrectified push-pull with channel attention for enhanced corruption robustness,

Reference 21

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Observation fa094813-9e56-4e25-adf3-58b74e9fbf87 · outbound

This paper cites Expert-guided ex- plainable few-shot learning for medical image diagnosis,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Expert-guided ex- plainable few-shot learning for medical image diagnosis,

Reference 22

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This paper cites GetNetUPAM: Ecologically Informed Nested Cross-Validation and Noise-Robust Attention for Marine Bioacoustic Monitoring.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI GetNetUPAM: Ecologically Informed Nested Cross-Validation and Noise-Robust Attention for Marine Bioacoustic Monitoring

Reference 23

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This paper cites Shape-aware thoracic edge map chest x- ray representation for pulmonary abnormality screening,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Shape-aware thoracic edge map chest x- ray representation for pulmonary abnormality screening,

Reference 24

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This paper cites Expert-guided ex- plainable few-shot learning with active sample selection for medical image analysis,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Expert-guided ex- plainable few-shot learning with active sample selection for medical image analysis,

Reference 25

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Observation 17e6e87b-f390-4c29-88ef-d4a356b0cd8e · outbound

This paper cites CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI CoSwin: Convolution Enhanced Hierarchical Shifted Window Attention For Small-Scale Vision

Reference 26

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Observation 308bf2a7-06f6-41e5-b35c-9e8d88885f42 · outbound

This paper cites Channel-selected stratified nested cross- validation for clinically relevant eeg-based parkinson’s disease detection,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Channel-selected stratified nested cross- validation for clinically relevant eeg-based parkinson’s disease detection,

Reference 28

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This paper cites Promoting shape bias in cnns: Frequency-based and contrastive regularization for corruption robustness,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Promoting shape bias in cnns: Frequency-based and contrastive regularization for corruption robustness,

Reference 30

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This paper cites Learning to select like humans: Explainable active learning for medical imaging,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Learning to select like humans: Explainable active learning for medical imaging,

Reference 31

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This paper cites Explainable Novel Category Discovery in Semantic Concept Space.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Explainable Novel Category Discovery in Semantic Concept Space

Reference 32

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This paper cites Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs

Reference 33

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This paper cites Frequency-aware contrastive learning for robust shape- biased convolutional neural networks,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Frequency-aware contrastive learning for robust shape- biased convolutional neural networks,

Reference 34

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This paper cites Large dimensional analysis of cooperative multicell precoding with local individual csi,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Large dimensional analysis of cooperative multicell precoding with local individual csi,

Reference 36

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

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 37

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This paper cites Pytorch library for cam methods,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Pytorch library for cam methods,

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This paper cites ImageNet Large Scale Visual Recognition Challenge,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI ImageNet Large Scale Visual Recognition Challenge,

Reference 39

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

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 40

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

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

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This paper cites Training data-efficient image trans- formers &; distillation through attention,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Training data-efficient image trans- formers &; distillation through attention,

Reference 44

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This paper cites Grad-CAM++: Generalized Gradient- Based Visual Explanations for Deep Convolutional Net- works ,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Grad-CAM++: Generalized Gradient- Based Visual Explanations for Deep Convolutional Net- works ,

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This paper cites Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization ,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization ,

Reference 49

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Full-gradient representation 18 for neural network visualization,

Reference 50

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This paper cites Axiom-based grad-cam: Towards accurate visualization and explanation of cnns,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Axiom-based grad-cam: Towards accurate visualization and explanation of cnns,

Reference 51

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This paper cites Learning Deep Features for Discriminative Lo- calization ,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Learning Deep Features for Discriminative Lo- calization ,

Reference 52

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This paper cites Boosting the transferability of adversarial attack on vision transformer with adaptive token tuning,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Boosting the transferability of adversarial attack on vision transformer with adaptive token tuning,

Reference 57

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This paper cites Emergent open-vocabulary semantic segmentation from off-the- shelf vision-language models,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Emergent open-vocabulary semantic segmentation from off-the- shelf vision-language models,

Reference 61

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This paper cites Enhancing prompt generation with adaptive refinement for camouflaged object detection,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Enhancing prompt generation with adaptive refinement for camouflaged object detection,

Reference 65

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This paper cites Quantifying attention flow in transformers,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Quantifying attention flow in transformers,

Reference 66

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This paper cites Unlike attention-map adaptations, the method operates on feature activations from the MLP in the final transformer blocks and uses the true-label class score as the gradient target.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Unlike attention-map adaptations, the method operates on feature activations from the MLP in the final transformer blocks and uses the true-label class score as the gradient target

Reference 67

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This paper cites the best way we found to apply GradCAM was to treat the last attention layer’s [CLS] token as the designated feature map,.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI the best way we found to apply GradCAM was to treat the last attention layer’s [CLS] token as the designated feature map,

Reference 68

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Unresolved cited work

Reference 69

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Observation b3a3ab5f-8b3b-4e34-9e52-235dae4b36c1 · outbound

This paper cites Rather than using an attention map as the attribution representation, the method operates on token-level feature activations.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Rather than using an attention map as the attribution representation, the method operates on token-level feature activations

Reference 70

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This paper cites Rather than operating on attention maps, it fuses gradients and intermediate ViT features from a selected transformer layer.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Rather than operating on attention maps, it fuses gradients and intermediate ViT features from a selected transformer layer

Reference 71

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Unresolved cited work

Reference 72

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This paper cites Align before Fuse: Vision and Language Representation Learning with Momentum Distillation [11]is best charac- terized as an attention-map-based attribution method.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Align before Fuse: Vision and Language Representation Learning with Momentum Distillation [11]is best charac- terized as an attention-map-based attribution method

Reference 73

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Observation ea63f919-8c90-4164-b286-bae2091db538 · outbound

This paper cites matching.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI matching

Reference 74

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI A dog on a white bed

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Grad-cam: Visual explana- tions from deep networks via gradient-based localiza- tion,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI BLIP: Bootstrap- ping language-image pre-training for unified vision- language understanding and generation,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Microsoft coco: Common objects in context,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Transformer in- terpretability beyond attention visualization,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Transreid: Transformer-based object re-identification,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Generic attentiemer- gent on-model explainability for interpreting bi-modal and encoder-decoder transformers,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Ia-redˆ2: Interpretability-aware redundancy reduction for vision transformers,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Analogous to evolutionary algorithm: Designing a unified sequence model,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Passive attention in artificial neural networks predicts human visual selectivity,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Vitae: Vision transformer advanced by exploring intrinsic inductive bias,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Align before fuse: Vision and language representation learning with momentum distillation,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI VLMAE: Vision-Language Masked Autoencoder

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI A compre- hensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes,

Reference 88

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI A challenging benchmark of anime style recognition,

Reference 89

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Metaformer is actually what you need for vision,

Reference 90

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Delving deep into the generalization of vision transformers under distribution shifts,

Reference 91

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI General facial representation learning in a visual- linguistic manner,

Reference 92

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Multi-modal alignment using representa- tion codebook,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Delving into sequential patches for deep- fake detection,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Adversarial normalization: I can visualize everything (ice),

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI A new benchmark: On the utility of synthetic data with blender for bare supervised learning and downstream domain adaptation,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Selfme: Self-supervised motion learning for micro- expression recognition,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Marlin: Masked autoencoder for facial video representation learning,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Blackvip: Black-box visual prompting for robust transfer learning,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Boost vision trans- former with gpu-friendly sparsity and quantization,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Shared inter- est...sometimes: Understanding the alignment between human perception, vision architectures, and saliency map techniques,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Semicvt: Semi- supervised convolutional vision transformer for seman- tic segmentation,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Masked autoencoding does not help natural language supervision at scale,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Vilem: Visual- language error modeling for image-text retrieval,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Improving visual grounding by encouraging consistent gradient-based explanations,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Multi-modal representation learn- ing with text-driven soft masks,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI From images to textual prompts: Zero-shot visual question answering with frozen large language models,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Sparse multi- modal vision transformer for weakly supervised seman- tic segmentation,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Semantic information in contrastive learning,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Smmix: Self-motivated image mixing for vision transformers,

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Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Cose: A consistency- sensitivity metric for saliency on image classification,

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