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We are excited to share that two of our papers have been accepted to ETRA 2026!
1. QualitEye: Public and Privacy-preserving Gaze Data Quality Verification
Mayar Elfares, Pascal Reisert, Ralf Kรผsters, Andreas Bulling
2. Learning Alignments of Human Gaze and Fine-grained Task Descriptions
Takumi Nishiyasu, Zhiming Hu, Andreas Bulling, Yoichi Sato
Congratulations to all authors!
For preprints and updates, feel free to visit our website: https://www.collaborative-ai.org/
Excited to share that our paper โRobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereoโ has been accepted to #ICLR2026 ๐.
We introduce RobustSpring, a new benchmark that evaluates not only accuracy but also robustness of optical flow, scene flow, and stereo models under 20 realโworld image corruptions.
Congratulations to the authors!
๐ For more news: https://www.collaborative-ai.org/publications/oei26_iclr/
Which part of graphs do people look at when solving analytical tasks?
๐ฐ Our work "Towards a Better Understanding of Graph Perception in Immersive Environments" was accepted to Graph Drawing #GD2025.
Congratulations to the authors!
Learn more about this work from our website: https://www.collaborative-ai.org/publications/zhang25_gd/
๐ A month of milestones! ๐ฐ ๐ฝ๐ฎ๐ฝ๐ฒ๐ฟ๐ got accepted at different venues.
๐ ๐๐ ๐ช๐จ๐ง: โ๐๐ฉ๐ณ๐ฐ๐ถ๐จ๐ฉ ๐ต๐ฉ๐ฆ ๐๐บ๐ฆ๐ด ๐ฐ๐ง ๐๐ฎ๐ฐ๐ต๐ช๐ฐ๐ฏ: ๐ ๐๐ถ๐ญ๐ต๐ช-๐ง๐ข๐ค๐ฆ๐ต๐ฆ๐ฅ ๐๐บ๐ฆ ๐๐ณ๐ข๐ค๐ฌ๐ช๐ฏ๐จ ๐๐ข๐ต๐ข๐ด๐ฆ๐ต ๐ง๐ฐ๐ณ ๐๐ฎ๐ฐ๐ต๐ช๐ฐ๐ฏ ๐๐ฆ๐ค๐ฐ๐จ๐ฏ๐ช๐ต๐ช๐ฐ๐ฏ ๐ช๐ฏ ๐๐ช๐ณ๐ต๐ถ๐ข๐ญ ๐๐ฆ๐ข๐ญ๐ช๐ต๐บโ
๐ ๐๐ฅ๐ข๐ฆ: โ๐๐ฏ๐ต๐ฆ๐ณ๐ข๐ค๐ต๐ช๐ท๐ฆ ๐๐น๐ฑ๐ณ๐ฆ๐ด๐ด๐ช๐ท๐ฆ ๐๐ฐ๐ต๐ช๐ฐ๐ฏ ๐๐ฆ๐ฏ๐ฆ๐ณ๐ข๐ต๐ช๐ฐ๐ฏ ๐๐ด๐ช๐ฏ๐จ ๐๐บ๐ฏ๐ข๐ฎ๐ช๐ค ๐๐ฐ๐ท๐ฆ๐ฎ๐ฆ๐ฏ๐ต ๐๐ณ๐ช๐ฎ๐ช๐ต๐ช๐ท๐ฆ๐ดโ
๐ ๐๐๐๐๐ฅ: โ๐๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ๐๐ฆ๐ข๐ฌ: ๐๐ฉ๐ข๐ต ๐๐ฐ๐ฆ๐ด ๐๐ถ๐ฎ๐ข๐ฏ ๐๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏ ๐๐ฆ๐ท๐ฆ๐ข๐ญ ๐๐ฃ๐ฐ๐ถ๐ต ๐๐ฏ๐ง๐ฐ๐ณ๐ฎ๐ข๐ต๐ช๐ฐ๐ฏ ๐๐ช๐ด๐ถ๐ข๐ญ๐ช๐ด๐ข๐ต๐ช๐ฐ๐ฏ?โ
๐ ๐ฉ๐๐ฆ: โ๐๐ฆ๐ญ๐ญ ๐๐ฆ ๐๐ช๐ต๐ฉ๐ฐ๐ถ๐ต ๐๐ฆ๐ญ๐ญ๐ช๐ฏ๐จ ๐๐ฆ: ๐๐ธ๐ฐ-๐๐ข๐บ ๐๐ณ๐ฆ๐ฅ๐ช๐ค๐ต๐ช๐ฐ๐ฏ ๐ฐ๐ง ๐๐ช๐ด๐ถ๐ข๐ญ๐ช๐ป๐ข๐ต๐ช๐ฐ๐ฏ ๐๐ช๐ต๐ฆ๐ณ๐ข๐ค๐บ ๐ข๐ฏ๐ฅ ๐๐ช๐ด๐ถ๐ข๐ญ ๐๐ต๐ต๐ฆ๐ฏ๐ต๐ช๐ฐ๐ฏโ
๐ Exciting News! ๐
HOIGaze: Gaze Estimation During Hand-Object Interactions in Extended Reality has been accepted to #SIGGRAPH 2025! ๐
HOIGaze introduces:1๏ธโฃ A hierarchical framework that first identifies which hand the user is visually attending to, then estimates gaze direction based on the handโs posture. 2๏ธโฃ A gaze estimation network that combines graph neural networks and cross-modal Transformers. 3๏ธโฃ An eye-head coordination loss function.
๐ Learn more: https://collaborative-ai.org/publications/hu25_siggraph/
๐ Exciting News! ๐
We're thrilled to share that our group's proposal, "๐ ๐๐น๐๐ถ๐ ๐ฒ๐ฑ๐ถ๐ฎ๐๐ฒ: ๐ ๐๐น๐๐ถ๐บ๐ผ๐ฑ๐ฎ๐น ๐๐ฒ๐ต๐ฎ๐๐ถ๐ผ๐๐ฟ ๐๐ป๐ฎ๐น๐๐๐ถ๐ ๐ณ๐ผ๐ฟ ๐๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ถ๐ฎ๐น ๐ ๐ฒ๐ฑ๐ถ๐ฎ๐๐ถ๐ผ๐ป", has been accepted as a Grand Challenge at ACM Multimedia 2025!๐
"The goal of this multi-year challenge is to contribute to realising the vision of autonomous artificial mediators by measurable advances in key conversational behaviour sensing and analysis tasks."
For more details, visit https://www.multimediate-challenge.org/.
๐ Exciting News! ๐ Our groupโs paper ๐ฉยฒ๐๐ถ๐ฎ๐น: ๐จ๐ป๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ผ๐ณ ๐ฉ๐ถ๐ฑ๐ฒ๐ผ ๐ฎ๐ป๐ฑ ๐ฉ๐ถ๐๐๐ฎ๐น ๐๐ถ๐ฎ๐น๐ผ๐ด ๐๐ถ๐ฎ ๐ ๐๐น๐๐ถ๐บ๐ผ๐ฑ๐ฎ๐น ๐๐ ๐ฝ๐ฒ๐ฟ๐๐ has been accepted at CVPR 2025! ๐๐
VยฒDial is a novel model specifically designed to handle both image and video input data for multimodal conversational tasks. Extensive evaluations on AVSD and VisDial datasets show that VยฒDial achieves new state-of-the-art results across multiple benchmarks.
Congratulations to the authors. ๐
๐ข New Paper Alert! ๐ข
We're thrilled to announce that our paper, HAIFAI: Human-AI Interaction for Mental Face Reconstruction, has been accepted by ACM Transactions on Interactive Intelligent Systems (TiiS).
Congratulations to the authors!
You can check out the preprint on arXiv https://arxiv.org/abs/2412.06323v1 and stay tuned for the camera-ready version on our website https://collaborative-ai.org/.
We present HAIFAI - a novel collaborative human-AI system to tackle the challenging task of reconstructing a visual representation of a face that exists only in a person's mind. Users iteratively rank images presented by the AI system based on their resemblance to a mental image. These rankings, in turn, allow the system to extract relevant image features, fuse them into a unified feature vector, and use a generative model to reconstruct the mental image. We also propose an extension called HAIFAI-X that allows users to manually refine and further improve the reconstruction using an easy-to-use slider interface. To avoid the need for tedious human data collection for model training, we introduce a computational user model of human ranking behaviour. For this, we collected a small face ranking dataset through an online crowd-sourcing study containing data from 275 participants. We evaluate HAIFAI and HAIFAI-X in a 12-participant user study and show that HAIFAI outperforms the previous state of the art regarding reconstruction quality, usability, perceived workload, and reconstruction speed. HAIFAI-X achieves even better reconstruction quality at the cost of reduced usability, perceived workload, and increased reconstruction time. We further validate the reconstructions in a subsequent face ranking study with 18 participants and show that HAIFAI-X achieves a new state-of-the-art identification rate of 60.6%. These findings represent a significant advancement towards developing new collaborative intelligent systems capable of reliably and effortlessly reconstructing a user's mental image.
๐ Exciting News! ๐
Our group has two papers (conditionally) accepted to #CHI2025.
1๏ธโฃ SummAct: Uncovering User Intentions Through Interactive Behaviour Summarisation
2๏ธโฃ How People Read Charts: A Model of Task-driven Eye Movement Control
Stay tuned for more details about the papers at: https://collaborative-ai.org/news/2025/01/two-paper-accepted-at-chi/
๐We are thrilled to share that our lab director Prof. @abulling has joined the editorial board of the IEEE Transactions on Visualization and Computer Graphics (TVCG) as of January 1, 2025.
Congratulations, Prof. Bulling! ๐
