Search Results for author:
Found 6 papers, 6 papers with code
An expert-driven data generation pipeline for histological images
Deep Learning (DL) models have been successfully applied to many applications including biomedical cell segmentation and classification in histological images.
Explaining Multi-modal Large Language Models by Analyzing their Vision Perception
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in understanding and generating content across various modalities, such as images and text.
Concept Visualization: Explaining the CLIP Multi-modal Embedding Using WordNet
Advances in multi-modal embeddings, and in particular CLIP, have recently driven several breakthroughs in Computer Vision (CV).
SE3D: A Framework For Saliency Method Evaluation In 3D Imaging
In these critical settings, explaining the model's decisions is fundamental.
Perception Visualization: Seeing Through the Eyes of a DNN
Results of our user study demonstrate that humans can better understand and predict the system's decisions when perception visualizations are available, thus easing the debugging and deployment of deep models as trusted systems.
Adversarial Scratches: Deployable Attacks to CNN Classifiers
We present Adversarial Scratches: a novel L0 black-box attack, which takes the form of scratches in images, and which possesses much greater deployability than other state-of-the-art attacks.
