Two papers at CVPR 2015
We will present the following two papers at the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2015)
 | Hosnieh Sattar; Sabine Müller; Mario Fritz; Andreas Bulling Prediction of Search Targets From Fixations in Open-World Settings Inproceedings Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), pp. 981-990, 2015. @inproceedings{sattar15_cvpr, title = {Prediction of Search Targets From Fixations in Open-World Settings}, author = {Hosnieh Sattar and Sabine Müller and Mario Fritz and Andreas Bulling}, url = {https://perceptual.mpi-inf.mpg.de/files/2015/04/sattar15_cvpr.pdf https://perceptual.mpi-inf.mpg.de/research/datasets/#sattar15_cvpr}, doi = {10.1109/CVPR.2015.7298700}, year = {2015}, date = {2015-03-02}, booktitle = {Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015)}, pages = {981-990}, abstract = {Previous work on predicting the target of visual search from human fixations only considered closed-world settings in which training labels are available and predictions are performed for a known set of potential targets. In this work we go beyond the state of the art by studying search target prediction in an open-world setting in which we no longer assume that we have fixation data to train for the search targets. We present a dataset containing fixation data of 18 users searching for natural images from three image categories within synthesised image collages of about 80 images. In a closed-world baseline experiment we show that we can predict the correct target image out of a candidate set of five images. We then present a new problem formulation for search target prediction in the open-world setting that is based on learning compatibilities between fixations and potential targets.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Previous work on predicting the target of visual search from human fixations only considered closed-world settings in which training labels are available and predictions are performed for a known set of potential targets. In this work we go beyond the state of the art by studying search target prediction in an open-world setting in which we no longer assume that we have fixation data to train for the search targets. We present a dataset containing fixation data of 18 users searching for natural images from three image categories within synthesised image collages of about 80 images. In a closed-world baseline experiment we show that we can predict the correct target image out of a candidate set of five images. We then present a new problem formulation for search target prediction in the open-world setting that is based on learning compatibilities between fixations and potential targets. |
 | Xucong Zhang; Yusuke Sugano; Mario Fritz; Andreas Bulling Appearance-Based Gaze Estimation in the Wild Inproceedings Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), pp. 4511-4520, 2015. @inproceedings{zhang15_cvpr, title = {Appearance-Based Gaze Estimation in the Wild}, author = {Xucong Zhang and Yusuke Sugano and Mario Fritz and Andreas Bulling}, url = {https://perceptual.mpi-inf.mpg.de/files/2015/04/zhang_CVPR15.pdf https://www.youtube.com/watch?v=rw6LZA1USG8 https://perceptual.mpi-inf.mpg.de/research/datasets/#zhang15_cvpr}, doi = {10.1109/CVPR.2015.7299081}, year = {2015}, date = {2015-03-02}, booktitle = {Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015)}, pages = {4511-4520}, abstract = {Appearance-based gaze estimation is believed to work well in real-world settings but existing datasets were collected under controlled laboratory conditions and methods were not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing datasets with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks, which significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation setting. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithm on three current datasets, including our own. This evaluation provides clear insights and allows us identify key research challenges of gaze estimation in the wild.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Appearance-based gaze estimation is believed to work well in real-world settings but existing datasets were collected under controlled laboratory conditions and methods were not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing datasets with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks, which significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation setting. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithm on three current datasets, including our own. This evaluation provides clear insights and allows us identify key research challenges of gaze estimation in the wild. |
