Four papers accepted at ETRA 2024

We are pleased to announce that the following four papers were accepted at the ACM Symposium of Eye Tracking Research & Applications (ETRA 2024). Moreover, the three accepted full papers will be published in the Proceedings of the ACM on Human-Computer Interaction (PACM HCI). Congratulations to the authors!

1. 

   PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation

   Mayar Elfares, Pascal Reisert, Zhiming Hu, Wenwu Tang, Ralf Küsters, Andreas Bulling

   Proc. ACM on Human-Computer Interaction (PACM HCI), 8(ETRA), pp. 1–22, 2024.

   Abstract Links BibTeX Project

   Latest gaze estimation methods require large-scale training data but their collection and exchange pose significant privacy risks. We propose PrivatEyes - the first privacy-enhancing training approach for appearance-based gaze estimation based on federated learning (FL) and secure multi-party computation (MPC). PrivatEyes enables training gaze estimators on multiple local datasets across different users and server-based secure aggregation of the individual estimators’ updates. PrivatEyes guarantees that individual gaze data remains private even if a majority of the aggregating servers is malicious. We also introduce a new data leakage attack DualView that shows that PrivatEyes limits the leakage of private training data more effectively than previous approaches. Evaluations on the MPIIGaze, MPIIFaceGaze, GazeCapture, and NVGaze datasets further show that the improved privacy does not lead to a lower gaze estimation accuracy or substantially higher computational costs - both of which are on par with its non-secure counterparts.

   doi:

   @article{elfares24_etra, title = {PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation}, author = {Elfares, Mayar and Reisert, Pascal and Hu, Zhiming and Tang, Wenwu and Küsters, Ralf and Bulling, Andreas}, year = {2024}, journal = {Proc. ACM on Human-Computer Interaction (PACM HCI)}, pages = {1--22}, volume = {8}, number = {ETRA}, doi = {} }
2. 

   Learning User Embeddings from Human Gaze for Personalised Saliency Prediction

   Florian Strohm, Mihai Bâce, Andreas Bulling

   Proc. ACM on Human-Computer Interaction (PACM HCI), 8(ETRA), pp. 1–18, 2024.

   Abstract Links BibTeX Project

   Reusable 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 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.

   doi:

   Paper: strohm24_etra.pdf

   @article{strohm24_etra, title = {Learning User Embeddings from Human Gaze for Personalised Saliency Prediction}, author = {Strohm, Florian and Bâce, Mihai and Bulling, Andreas}, year = {2024}, journal = {Proc. ACM on Human-Computer Interaction (PACM HCI)}, pages = {1--18}, volume = {8}, number = {ETRA}, doi = {} }
3. 

   VisRecall++: Analysing and Predicting Visualisation Recallability from Gaze Behaviour

   Yao Wang, Yue Jiang, Zhiming Hu, Constantin Ruhdorfer, Mihai Bâce, Andreas Bulling

   Proc. ACM on Human-Computer Interaction (PACM HCI), 8(ETRA), pp. 1–18, 2024.

   Abstract Links BibTeX Project

   Question answering has recently been proposed as a promising means to assess the recallability of information visualisations. However, prior works are yet to study the link between visually encoding a visualisation in memory and recall performance. To fill this gap, we propose VisRecall++ – a novel 40-participant recallability dataset that contains gaze data on 200 visualisations and five question types, such as identifying the title, and finding extreme values.We measured recallability by asking participants questions after they observed the visualisation for 10 seconds.Our analyses reveal several insights, such as saccade amplitude, number of fixations, and fixation duration significantly differ between high and low recallability groups.Finally, we propose GazeRecallNet – a novel computational method to predict recallability from gaze behaviour that outperforms several baselines on this task.Taken together, our results shed light on assessing recallability from gaze behaviour and inform future work on recallability-based visualisation optimisation.

   doi: 10.1145/3655613

   Paper: wang24_etra.pdf

   Supplementary Material: wang24_etra_sup.pdf

   @article{wang24_etra, title = {VisRecall++: Analysing and Predicting Visualisation Recallability from Gaze Behaviour}, author = {Wang, Yao and Jiang, Yue and Hu, Zhiming and Ruhdorfer, Constantin and Bâce, Mihai and Bulling, Andreas}, year = {2024}, journal = {Proc. ACM on Human-Computer Interaction (PACM HCI)}, pages = {1--18}, volume = {8}, number = {ETRA}, doi = {10.1145/3655613} }

1. 

   Saliency3D: a 3D Saliency Dataset Collected on Screen

   Yao Wang, Qi Dai, Mihai Bâce, Karsten Klein, Andreas Bulling

   Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), pp. 1–9, 2024.

   Abstract Links BibTeX Project

   While visual saliency has recently been studied in 3D, the experimental setup for collecting 3D saliency data can be expensive and cumbersome. To address this challenge, we propose a novel experimental design that utilizes an eye tracker on a screen to collect 3D saliency data. Our experimental design reduces the cost and complexity of 3D saliency dataset collection. We first collect gaze data on a screen, then we map them to 3D saliency data through perspective transformation. Using this method, we collect a 3D saliency dataset (49,276 fixations) comprising 10 participants looking at sixteen objects. Moreover, we examine the viewing preferences for objects and discuss our findings in this study. Our results indicate potential preferred viewing directions and a correlation between salient features and the variation in viewing directions.

   doi: 10.1145/3649902.3653350

   Paper: wang24_etras.pdf

   @inproceedings{wang24_etras, title = {Saliency3D: a 3D Saliency Dataset Collected on Screen}, author = {Wang, Yao and Dai, Qi and B{\^a}ce, Mihai and Klein, Karsten and Bulling, Andreas}, year = {2024}, pages = {1--9}, booktitle = {Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA)}, doi = {10.1145/3649902.3653350} }

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