Fish stock assessment often involves manual fish counting by taxonomy
specialists, which is both time-consuming and costly. We propose an automated
computer vision system that performs both taxonomic classification and fish
size estimation from images taken with a low-cost digital camera. The system
first performs object detection and segmentation using a Mask R-CNN to identify
individual fish from images containing multiple fish, possibly consisting of
different species. Then each fish species is classified and the predicted
length using separate machine learning models. These models are trained on a
dataset of 50,000 hand-annotated images containing 163 different fish species,
ranging in length from 10cm to 250cm. Evaluated on held-out test data, our
system achieves a $92\%$ intersection over union on the fish segmentation task,
a $89\%$ top-1 classification accuracy on single fish species classification,
and a $2.3$~cm mean error on the fish length estimation task.

提出了一种基于计算机视觉的自动化鱼类数量评估系统，该系统能够通过低成本数字相机拍摄的图像进行鱼类分类和尺寸估计，训练模型在手动标注的 50000 张图像数据集上训练，实现了鱼类分割、物种分类和长度估计任务的高精度。

FishNet：用于低成本鱼类存量估计的深度神经网络

FishNet: Deep Neural Networks for Low-Cost Fish Stock Estimation

Automated computer vision systems have been applied in many domains including
security, law enforcement, and personal devices, but recent reports suggest
that these systems may produce biased results, discriminating against people in
certain demographic groups. Diagnosing and understanding the underlying true
causes of model biases, however, are challenging tasks because modern computer
vision systems rely on complex black-box models whose behaviors are hard to
decode. We propose to use an encoder-decoder network developed for image
attribute manipulation to synthesize facial images varying in the dimensions of
gender and race while keeping other signals intact. We use these synthesized
images to measure counterfactual fairness of commercial computer vision
classifiers by examining the degree to which these classifiers are affected by
gender and racial cues controlled in the images, e.g., feminine faces may
elicit higher scores for the concept of nurse and lower scores for STEM-related
concepts. We also report the skewed gender representations in an online search
service on profession-related keywords, which may explain the origin of the
biases encoded in the models.

本文提出了一种使用编码器 - 解码器网络开发的人脸图像合成方法，以测量商业计算机视觉分类器的反事实公平性，并报告了在线搜索服务中与职业相关的关键字中出现的偏向性，用以解释模型偏差的来源。