Fashion is one of the largest world's industries and computer vision
techniques have been becoming more popular in recent years, in particular, for
tasks such as object detection and apparel segmentation. Even with the rapid
growth in computer vision solutions, specifically for the fashion industry,
many problems are far for being resolved. Therefore, not at all times,
adjusting out-of-the-box pre-trained computer vision models will provide the
desired solution. In the present paper is proposed a pipeline that takes a
noisy image with a person and specifically detects the regions with garments
that are bottoms or tops. Our solution implements models that are capable of
finding human parts in an image e.g. full-body vs half-body, or no human is
found. Then, other models knowing that there's a human and its composition
(e.g. not always we have a full-body) finds the bounding boxes/regions of the
image that very likely correspond to a bottom or a top. For the creation of
bounding boxes/regions task, a benchmark dataset was specifically prepared. The
results show that the Mask RCNN solution is robust, and generalized enough to
be used and scalable in unseen apparel/fashion data.

本文提出一种使用计算机视觉技术实现服装图像分割的方法，该方法基于经过训练的模型自动检测人体部位，并使用 Mask RCNN 算法对图像中的下装和上装进行检测和划分，取得了较好的效果。

时装上下装物体检测

Fashion Object Detection for Tops & Bottoms

Making top-down human pose estimation method present both good performance
and high efficiency is appealing. Mask RCNN can largely improve the efficiency
by conducting person detection and pose estimation in a single framework, as
the features provided by the backbone are able to be shared by the two tasks.
However, the performance is not as good as traditional two-stage methods. In
this paper, we aim to largely advance the human pose estimation results of
Mask-RCNN and still keep the efficiency. Specifically, we make improvements on
the whole process of pose estimation, which contains feature extraction and
keypoint detection. The part of feature extraction is ensured to get enough and
valuable information of pose. Then, we introduce a Global Context Module into
the keypoints detection branch to enlarge the receptive field, as it is crucial
to successful human pose estimation. On the COCO val2017 set, our model using
the ResNet-50 backbone achieves an AP of 68.1, which is 2.6 higher than Mask
RCNN (AP of 65.5). Compared to the classic two-stage top-down method
SimpleBaseline, our model largely narrows the performance gap (68.1 AP vs. 68.9
AP) with a much faster inference speed (77 ms vs. 168 ms), demonstrating the
effectiveness of the proposed method. Code is available at:
this https URL

本文针对使用 Mask RCNN 进行人体姿态估计时存在的性能问题及效率问题，提出了一种基于改进后的全局上下文模块和 ResNet-50 骨架的人体姿态估计方法，取得了较好的姿态估计效果和运行效率。

面向高性能的单阶段人体姿态估计

Towards High Performance One-Stage Human Pose Estimation

Many existing techniques provide automatic estimation of crop damage due to
various diseases. However, early detection can prevent or reduce the extend of
damage itself. The limited performance of existing techniques in early
detection is lack of localized information. We instead propose a dataset with
annotations for each diseased segment in each image. Unlike existing
approaches, instead of classifying images into either healthy or diseased, we
propose to provide localized classification for each segment of an images. Our
method is based on Mask RCNN and provides location as well as extend of
infected regions on the plant. Thus the extend of damage on the crop can be
estimated. Our method has obtained overall 87.6% accuracy on the proposed
dataset as compared to 58.4% obtained without incorporating localized
information.

提出基于 Mask RCNN 的作物病害本地化分类方法，用于早期检测，可提供病变区域的位置和范围估计，相对现有方法，本方法对作物病损区域的准确率达到了 87.6%。