Neural processes (NPs) aim to stochastically complete unseen data points
based on a given context dataset. NPs essentially leverage a given dataset as a
context representation to derive a suitable identifier for a novel task. To
improve the prediction accuracy, many variants of NPs have investigated context
embedding approaches that generally design novel network architectures and
aggregation functions satisfying permutation invariant. In this work, we
propose a stochastic attention mechanism for NPs to capture appropriate context
information. From the perspective of information theory, we demonstrate that
the proposed method encourages context embedding to be differentiated from a
target dataset, allowing NPs to consider features in a target dataset and
context embedding independently. We observe that the proposed method can
appropriately capture context embedding even under noisy data sets and
restricted task distributions, where typical NPs suffer from a lack of context
embeddings. We empirically show that our approach substantially outperforms
conventional NPs in various domains through 1D regression, predator-prey model,
and image completion. Moreover, the proposed method is also validated by
MovieLens-10k dataset, a real-world problem.

该研究提出了一种基于随机注意力机制的神经过程方法来捕捉适当的上下文信息，从信息论的角度证明了该方法鼓励上下文嵌入与目标数据集不同，可以实现上下文嵌入和目标数据集中的特征独立处理，在各种领域的实验中，该方法明显优于传统的神经过程方法。

带有随机注意力机制的神经过程：更多关注上下文数据集

Neural Processes with Stochastic Attention: Paying more attention to the context dataset

State-of-the-art two-stage object detectors apply a classifier to a sparse
set of object proposals, relying on region-wise features extracted by RoIPool
or RoIAlign as inputs. The region-wise features, in spite of aligning well with
the proposal locations, may still lack the crucial context information which is
necessary for filtering out noisy background detections, as well as recognizing
objects possessing no distinctive appearances. To address this issue, we
present a simple but effective Hierarchical Context Embedding (HCE) framework,
which can be applied as a plug-and-play component, to facilitate the
classification ability of a series of region-based detectors by mining
contextual cues. Specifically, to advance the recognition of context-dependent
object categories, we propose an image-level categorical embedding module which
leverages the holistic image-level context to learn object-level concepts.
Then, novel RoI features are generated by exploiting hierarchically embedded
context information beneath both whole images and interested regions, which are
also complementary to conventional RoI features. Moreover, to make full use of
our hierarchical contextual RoI features, we propose the early-and-late fusion
strategies (i.e., feature fusion and confidence fusion), which can be combined
to boost the classification accuracy of region-based detectors. Comprehensive
experiments demonstrate that our HCE framework is flexible and generalizable,
leading to significant and consistent improvements upon various region-based
detectors, including FPN, Cascade R-CNN and Mask R-CNN.

提出了一种基于 Hierarchical Context Embedding（HCE）框架的对象检测方法，该方法通过挖掘上下文线索来增强区域感知检测器的分类能力。HCE 包含一个图像级别分类嵌入模块和一个区域感知分类嵌入模块，两个模块分别从整体和局部两个方面提取特征，并通过两种策略实现早期特征合并和分类器信任度合并，以提高区域感知检测器的检测性能。

基于区域的物体检测的分层上下文嵌入

Hierarchical Context Embedding for Region-based Object Detection

Human parsing has received considerable interest due to its wide application
potentials. Nevertheless, it is still unclear how to develop an accurate human
parsing system in an efficient and elegant way. In this paper, we identify
several useful properties, including feature resolution, global context
information and edge details, and perform rigorous analyses to reveal how to
leverage them to benefit the human parsing task. The advantages of these useful
properties finally result in a simple yet effective Context Embedding with Edge
Perceiving (CE2P) framework for single human parsing. Our CE2P is end-to-end
trainable and can be easily adopted for conducting multiple human parsing.
Benefiting the superiority of CE2P, we achieved the 1st places on all three
human parsing benchmarks. Without any bells and whistles, we achieved 56.50\%
(mIoU), 45.31\% (mean $AP^r$) and 33.34\% ($AP^p_{0.5}$) in LIP, CIHP and MHP
v2.0, which outperform the state-of-the-arts more than 2.06\%, 3.81\% and
1.87\%, respectively. We hope our CE2P will serve as a solid baseline and help
ease future research in single/multiple human parsing. Code has been made
available at https://github.com/liutinglt/CE2P.

通过分析解剖学特征、全局上下文信息和边缘细节等多种因素，本研究提出了一种简单而有效的 “上下文嵌入与边缘感知框架（CE2P）”，用于单人和多人解剖学。CE2P 在多个基准测试中表现出了优越的性能，并希望它能成为未来单人 / 多人解剖学研究的坚实基础。