← Search

Thomas S. A. Wallis

4 accepted papers

2021

Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization

ICLR 2021poster

Feature visualizations such as synthetic maximally activating images are a widely used explanation method to better understand the information processing of convolutional neural networks (CNNs). At the same time, there are concerns that these visualizations might not accurately represent CNNs' inner…

2021

How Well do Feature Visualizations Support Causal Understanding of CNN Activations?

NeurIPS 2021spotlight

A precise understanding of why units in an artificial network respond to certain stimuli would constitute a big step towards explainable artificial intelligence. One widely used approach towards this goal is to visualize unit responses via activation maximization. These feature visualizations are pu…

2018

Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics

ECCV 2018poster

Dozens of new models on fixation prediction are published every year and compared on open benchmarks such as MIT300 and LSUN. However, progress in the field can be difficult to judge because models are compared using a variety of inconsistent metrics. Here we show that no single saliency map can per…

2017

Understanding Low- and High-Level Contributions to Fixation Prediction

ICCV 2017poster

Understanding where people look in images is an important problem in computer vision. Despite significant research, it remains unclear to what extent human fixations can be predicted by low-level (contrast) compared to high-level (presence of objects) image features. Here we address this problem by…

Cited by 374PDFScholar