Possibilities of eye tracking and EEG integration for visual search on 2D maps

This on-going research paper explores (the possibilities to integrate eye tracking (ET) and electroencephalogram (EEG) for cartographic usability research. While ET, on one hand, provides observations and measurements related to gaze movements, EEG, on the other hand, helps to monitor and measure electrical activity occurring at different locations in the brain with a high temporal resolution. Therefore, combining ET and EEG introduces a holistic approach enabling to measure both overt and covert attention, and additionally, may reveal insights on individual’s different strategies of spatial cognition, if there is any. In this context, we introduce the experimental design settings for visual search task on simplified 2D static maps considering expert and novice participants, outlining methodological proposal and possible analyses. The paper mainly discusses the technical and theoretical issues of ET-EEG integration and mentions potential benefits of implementing EEG in cartographic usability research to indicate its value for future studies

Objects predict fixations better than early saliency

Humans move their eyes while looking at scenes and pictures. Eye movements correlate with shifts in attention and are thought to be a consequence of optimal resource allocation for high-level tasks such as visual recognition. Models of attention, such as “saliency maps,” are often built on the assumption that “early” features (color, contrast, orientation, motion, and so forth) drive attention directly. We explore an alternative hypothesis: Observers attend to “interesting” objects. To test this hypothesis, we measure the eye position of human observers while they inspect photographs of common natural scenes. Our observers perform different tasks: artistic evaluation, analysis of content, and search. Immediately after each presentation, our observers are asked to name objects they saw. Weighted with recall frequency, these objects predict fixations in individual images better than early saliency, irrespective of task. Also, saliency combined with object positions predicts which objects are frequently named. This suggests that early saliency has only an indirect effect on attention, acting through recognized objects. Consequently, rather than treating attention as mere preprocessing step for object recognition, models of both need to be integrated

Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation

We propose an approach to discover class-specific pixels for the weakly-supervised semantic segmentation task. We show that properly combining saliency and attention maps allows us to obtain reliable cues capable of significantly boosting the performance. First, we propose a simple yet powerful hierarchical approach to discover the class-agnostic salient regions, obtained using a salient object detector, which otherwise would be ignored. Second, we use fully convolutional attention maps to reliably localize the class-specific regions in a given image. We combine these two cues to discover class-specific pixels which are then used as an approximate ground truth for training a CNN. While solving the weakly supervised semantic segmentation task, we ensure that the image-level classification task is also solved in order to enforce the CNN to assign at least one pixel to each object present in the image. Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of 60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to the published state-of-the-art results. The code is made publicly available

Gaze Distribution Analysis and Saliency Prediction Across Age Groups

Knowledge of the human visual system helps to develop better computational models of visual attention. State-of-the-art models have been developed to mimic the visual attention system of young adults that, however, largely ignore the variations that occur with age. In this paper, we investigated how visual scene processing changes with age and we propose an age-adapted framework that helps to develop a computational model that can predict saliency across different age groups. Our analysis uncovers how the explorativeness of an observer varies with age, how well saliency maps of an age group agree with fixation points of observers from the same or different age groups, and how age influences the center bias. We analyzed the eye movement behavior of 82 observers belonging to four age groups while they explored visual scenes. Explorativeness was quantified in terms of the entropy of a saliency map, and area under the curve (AUC) metrics was used to quantify the agreement analysis and the center bias. These results were used to develop age adapted saliency models. Our results suggest that the proposed age-adapted saliency model outperforms existing saliency models in predicting the regions of interest across age groups