| Are Data-Driven Explanations Robust Against Out-of-Distribution Data? |
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| Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior |
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| Teaching Matters: Investigating the Role of Supervision in Vision Transformers |
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| Adversarial Counterfactual Visual Explanations |
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| SketchXAI: A First Look at Explainability for Human Sketches |
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| Doubly Right Object Recognition: A why Prompt for Visual Rationales |
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| Overlooked Factors in Concept-based Explanations: Dataset Choice, Concept Learnability, and Human Capability |
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| Initialization Noise in Image Gradients and Saliency Maps |
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| Learning Bottleneck Concepts in Image Classification |
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| Zero-Shot Model Diagnosis |
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| OCTET: Object-Aware Counterfactual Explanations |
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| X-Pruner: eXplainable Pruning for Vision Transformers |
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| Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis |
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| CRAFT: Concept Recursive Activation FacTorization for Explainability |
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| Grounding Counterfactual Explanation of Image Classifiers to Textual Concept Space |
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| Explaining Image Classifiers with Multiscale Directional Image Representation |
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| IDGI: A Framework to Eliminate Explanation Noise from Integrated Gradients |
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| Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification |
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| Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning |
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| PIP-Net: Patch-based Intuitive Prototypes for Interpretable Image Classification |
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| Shortcomings of Top-Down Randomization-based Sanity Checks for Evaluations of Deep Neural Network Explanations |
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| Spatial-Temporal Concept based Explanation of 3D ConvNets |
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| A Practical Upper Bound for the Worst-Case Attribution Deviations |
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| Adversarial Normalization: I Can Visualize Everything (ICE) |
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