Description
Book SynopsisFALCON: Fair Active Learning for Content Moderation.- Generalizing Fairness to Generative Language Models via Reformulation of Non-discrimination Criteria.- Beyond the Surface: A Comprehensive Analysis of Implicit Bias in Vision-Language Models.- Fairness of AI Systems in the Legal Context.- DebiasPI: Inference-time Debiasing by Prompt Iteration of a Text-to-Image Generative Model.- Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognition.- Prompt and Prejudice.- Localization-Guided Supervision for Robust Medical Image Classification by Vision Transformers.- Top-GAP: Integrating Size Priors in CNNs for more Interpretability, Robustness, and Bias Mitigation.- Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and Transformers.-
An Investigation on The Position Encoding in Vision-Based Dynamics Prediction.- What could go wrong? Discovering and describing failure modes in computer vision.- Image-guided topic modeling for interpretable privacy classification.- Integrating Local and Global Interpretability for Deep Concept-Based Reasoning Models.- From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation.- Feature Contribution in Monocular Depth Estimation.- Concept-Based Explanations in Computer Vision: Where Are We and Where Could We Go?.- Explanation Alignment: Quantifying the Correctness of Model Reasoning At Scale.- Detect Fake with Fake: Leveraging Synthetic Data-driven Representation
for Synthetic Image Detection.- Incremental and Decremental Continual Learning for Privacy-preserving
Video Recognition.- Exploring Strengths and Weaknesses of Super-Resolution Attack in Deepfake
Detection.- Are CLIP features all you need for Universal Synthetic Image Origin Attribution?.- GLoFool: global enhancements and local perturbations to craft adversarial images.- Evolution of Detection Performance throughout the Online Lifespan of Synthetic Images.- Your diffusion model is an implicit synthetic image detector.- The Phantom Menace: Unmasking Privacy Leakages in Vision-Language
Models.
