Multi-label image classification, which can be categorized into
label-dependency and region-based methods, is a challenging problem due to the
complex underlying object layouts. Although region-based methods are less
likely to encounter issues with model generalizability than label-dependency
methods, they often generate hundreds of meaningless or noisy proposals with
non-discriminative information, and the contextual dependency among the
localized regions is often ignored or over-simplified. This paper builds a
unified framework to perform effective noisy-proposal suppression and to
interact between global and local features for robust feature learning.
Specifically, we propose category-aware weak supervision to concentrate on
non-existent categories so as to provide deterministic information for local
feature learning, restricting the local branch to focus on more high-quality
regions of interest. Moreover, we develop a cross-granularity attention module
to explore the complementary information between global and local features,
which can build the high-order feature correlation containing not only
global-to-local, but also local-to-local relations. Both advantages guarantee a
boost in the performance of the whole network. Extensive experiments on two
large-scale datasets (MS-COCO and VOC 2007) demonstrate that our framework
achieves superior performance over state-of-the-art methods.

该研究构建了一个统一框架，以执行有效的噪声提议抑制并相互作用全局和局部特征进行稳健的特征学习，包括使用类别感知弱监督来集中注意力于不存在的类别为本地特征学习提供确定性信息，以及开发跨粒度注意力模块来探索全局和局部特征之间的互补信息。在 MS-COCO 和 VOC 2007 两个大规模数据集上的广泛实验表明，该框架优于最先进的方法。

全局与局部的结合：基于类别感知弱监督的有效多标签图像分类

Global Meets Local: Effective Multi-Label Image Classification via  Category-Aware Weak Supervision

Face detection has achieved great success using the region-based methods. In
this report, we propose a region-based face detector applying deep networks in
a fully convolutional fashion, named Face R-FCN. Based on Region-based Fully
Convolutional Networks (R-FCN), our face detector is more accurate and
computational efficient compared with the previous R-CNN based face detectors.
In our approach, we adopt the fully convolutional Residual Network (ResNet) as
the backbone network. Particularly, We exploit several new techniques including
position-sensitive average pooling, multi-scale training and testing and
on-line hard example mining strategy to improve the detection accuracy. Over
two most popular and challenging face detection benchmarks, FDDB and WIDER
FACE, Face R-FCN achieves superior performance over state-of-the-arts.

本报告提出了一种基于深度网络的区域检测人脸的新方法 ——Face R-FCN，使用全卷积神经网络做到更准确和更高效的人脸检测，我们采用了一些新技术，包括位置敏感平均池化，多尺度训练和测试以及在线困难样例挖掘策略，这种方法在 FDDB 和 WIDER FACE 等标准数据集上达到了 state-of-the-artlevel 的检测效果。

基于区域的全卷积网络检测人脸

Detecting Faces Using Region-based Fully Convolutional Networks

Region-based methods have proven necessary for improving segmentation
accuracy of neuronal structures in electron microscopy (EM) images. Most
region-based segmentation methods use a scoring function to determine region
merging. Such functions are usually learned with supervised algorithms that
demand considerable ground truth data, which are costly to collect. We propose
a semi-supervised approach that reduces this demand. Based on a merge tree
structure, we develop a differentiable unsupervised loss term that enforces
consistent predictions from the learned function. We then propose a Bayesian
model that combines the supervised and the unsupervised information for
probabilistic learning. The experimental results on three EM data sets
demonstrate that by using a subset of only 3% to 7% of the entire ground truth
data, our approach consistently performs close to the state-of-the-art
supervised method with the full labeled data set, and significantly outperforms
the supervised method with the same labeled subset.

本文提出一种基于区域的半监督分割方法，通过一个可微的无监督损失函数和贝叶斯模型结合有监督信息，可以在仅用 3％到 7％的完整标签数据子集的情况下实现与全标签方法相当的表现并显著优于仅相同标签子集的有监督方法。