Dogs have a unique evolutionary relationship with humans and serve many
important roles e.g. search and rescue, blind assistance, emotional support.
However, few datasets exist to categorize visual features and objects available
to dogs, as well as how dogs direct their visual attention within their
environment. We collect and study a dataset with over 11,698 gazes to
categorize the objects available to be gazed at by 11 dogs in everyday outdoor
environments i.e. a walk around a college campus and urban area. We explore the
availability of these object categories and the visual attention of dogs over
these categories using a head mounted eye tracking apparatus. A small portion
(approx. 600 images or < 20% of total dataset) of the collected data is used to
fine tune a MaskRCNN for the novel image domain to segment objects present in
the scene, enabling further statistical analysis on the visual gaze tendencies
of dogs. The MaskRCNN, with eye tracking apparatus, serves as an end to end
model for automatically classifying the visual fixations of dogs. The fine
tuned MaskRCNN performs far better than chance. There are few individual
differences between the 11 dogs and we observe greater visual fixations on
buses, plants, pavement, and construction equipment. This work takes a step
towards understanding visual behavior of dogs and their interaction with the
physical world.

研究了 11 只狗的可视注视及其对不同物体的视觉注意力，使用头戴式眼动仪和训练好的 MaskRCNN 模型对数据进行了分析，结果显示狗更多地注视公交车、植物、人行道和建筑设备。

狗类视觉环境分类与视觉注意分析

Categorizing the Visual Environment and Analyzing the Visual Attention  of Dogs

The human ear is generally universal, collectible, distinct, and permanent.
Ear-based biometric recognition is a niche and recent approach that is being
explored. For any ear-based biometric algorithm to perform well, ear detection
and segmentation need to be accurately performed. While significant work has
been done in existing literature for bounding boxes, a lack of approaches
output a segmentation mask for ears. This paper trains and compares three newer
models to the state-of-the-art MaskRCNN (ResNet 101 +FPN) model across four
different datasets. The Average Precision (AP) scores reported show that the
newer models outperform the state-of-the-art but no one model performs the best
over multiple datasets.

本文研究使用基于耳朵的生物识别技术，探讨耳朵检测和分割的关键算法，训练和比较三种新模型与现有的 MaskRCNN 模型，并根据四个数据集的平均精度得分 (AP) 表明，新模型表现更好，但是单一模型无法最佳地适应多个数据集。