ABSTRACT
Attention is an important attribute of human vision for study of user's quality of experience (QoE). The attention information collection from eye tracking is impossible in the current scenario of Covid-19. Different mouse metaphors have been proposed to study visual attention without eye tracking equipment. These methods have shown promising results on different types of images (visualizations, natural images and websites) with well-identified regions of interest. However, they have not been precisely tested for QoE applications, where natural images are processed with different algorithms (compression, tone-mapping, etc.) and visual content can induce more exploratory behavior. This paper studies and compares different configurations of bubble view metaphors for the study of visual attention in tone-mapped images. © 2021 IEEE.
ABSTRACT
Confinement during COVID-19 has caused serious effects on agriculture all over the world. As one of the efficient solutions, mechanical harvest/auto-harvest that is based on object detection and robotic harvester becomes an urgent need. Within the auto-harvest system, robust few-shot object detection model is one of the bottlenecks, since the system is required to deal with new vegetable/fruit categories and the collection of large-scale annotated datasets for all the novel categories is expensive. There are many few-shot object detection models that were developed by the community. Yet whether they could be employed directly for real life agricultural applications is still questionable, as there is a context-gap between the commonly used training datasets and the images collected in real life agricultural scenarios. To this end, in this study, we present a novel cucumber dataset and propose two data augmentation strategies that help to bridge the context-gap. Experimental results show that 1) the state-of-the-art few-shot object detection model performs poorly on the novel 'cucumber' category;and 2) the proposed augmentation strategies outperform the commonly used ones.