05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Usable and fast interactive mental face reconstruction(2023) Strohm, Florian; Bâce, Mihai; Bulling, AndreasWe introduce an end-to-end interactive system for mental face reconstruction - the challenging task of visually reconstructing a face image a person only has in their mind. In contrast to existing methods that suffer from low usability and high mental load, our approach only requires the user to rank images over multiple iterations according to the perceived similarity with their mental image. Based on these rankings, our mental face reconstruction system extracts image features in each iteration, combines them into a joint feature vector, and then uses a generative model to visually reconstruct the mental image. To avoid the need for collecting large amounts of human training data, we further propose a computational user model that can simulate human ranking behaviour using data from an online crowd-sourcing study (N=215). Results from a 12-participant user study show that our method can reconstruct mental images that are visually similar to existing approaches but has significantly higher usability, lower perceived workload, and is faster. In addition, results from a third 22-participant lineup study in which we validated our reconstructions on a face ranking task show a identification rate of , which is in line with prior work. These results represent an important step towards new interactive intelligent systems that can robustly and effortlessly reconstruct a user’s mental image.Item Open Access Learning user embeddings from human gaze for personalised saliency prediction(2024) Strohm, Florian; Bâce, Mihai; Bulling, AndreasReusable embeddings of user behaviour have shown significant performance improvements for the personalised saliency prediction task. However, prior works require explicit user characteristics and preferences as input, which are often difficult to obtain. We present a novel method to extract user embeddings from pairs of natural images and corresponding saliency maps generated from a small amount of user-specific eye tracking data. At the core of our method is a Siamese convolutional neural encoder that learns the user embeddings by contrasting the image and personal saliency map pairs of different users. Evaluations on two public saliency datasets show that the generated embeddings have high discriminative power, are effective at refining universal saliency maps to the individual users, and generalise well across users and images. Finally, based on our model's ability to encode individual user characteristics, our work points towards other applications that can benefit from reusable embeddings of gaze behaviour.