Attention Visualization

Attention visualization techniques aim to enhance the interpretability of complex deep learning models, particularly transformers, by revealing which input features most influence model predictions. Current research focuses on applying these techniques to various model architectures, including vision transformers (ViTs) and encoder-only transformers, and across diverse data modalities such as images, text, and point clouds, often employing methods like gradient-based and attention-based visualizations. These visualizations are proving valuable for improving model understanding, identifying problematic model behaviors like interference in multilingual models, and even guiding model improvements, ultimately contributing to more reliable and trustworthy AI systems across numerous applications.