3 citations · 3 across the 5 of their papers we have counts for
6 papers
Which private attributes do VLMs agree on and predict well?
Olena Hrynenko, Darya Baranouskaya, Alina Elena Baia +1
Visual Language Models (VLMs) are often used for zero-shot detection of visual attributes in the image. We present a zero-shot evaluation of open-source VLMs for privacy-related at…
Cross-modal Counterfactual Explanations: Uncovering Decision Factors and Dataset Biases in Subjective Classification
Alina Elena Baia, Andrea Cavallaro
Concept-driven counterfactuals explain decisions of classifiers by altering the model predictions through semantic changes. In this paper, we present a novel approach that leverage…
On the Robustness of Vision-Language Models in Zero-shot Privacy Classification
Alina Elena Baia, Alessio Xompero, Andrea Cavallaro
Automatic systems for document understanding require multimodal models that accurately identify sensitive visual content, even in the presence of image degradations. Instruction-fo…
Specializing General-purpose LLM Embeddings for Implicit Hate Speech Detection across Datasets
Vassiliy Cheremetiev, Quang Long Ho Ngo, Chau Ying Kot +2
Implicit hate speech (IHS) is indirect language that conveys prejudice or hatred through subtle cues, sarcasm or coded terminology. IHS is challenging to detect as it does not incl…
Image-guided topic modeling for interpretable privacy classification
Alina Elena Baia, Andrea Cavallaro
Predicting and explaining the private information contained in an image in human-understandable terms is a complex and contextual task. This task is challenging even for large lang…
Black-box Attacks on Image Activity Prediction and its Natural Language Explanations
Alina Elena Baia, Valentina Poggioni, Andrea Cavallaro
Explainable AI (XAI) methods aim to describe the decision process of deep neural networks. Early XAI methods produced visual explanations, whereas more recent techniques generate m…