In this paper, we present a novel recurrent multi-view stereo network based
on long short-term memory (LSTM) with adaptive aggregation, namely AA-RMVSNet.
We firstly introduce an intra-view aggregation module to adaptively extract
image features by using context-aware convolution and multi-scale aggregation,
which efficiently improves the performance on challenging regions, such as thin
objects and large low-textured surfaces. To overcome the difficulty of varying
occlusion in complex scenes, we propose an inter-view cost volume aggregation
module for adaptive pixel-wise view aggregation, which is able to preserve
better-matched pairs among all views. The two proposed adaptive aggregation
modules are lightweight, effective and complementary regarding improving the
accuracy and completeness of 3D reconstruction. Instead of conventional 3D
CNNs, we utilize a hybrid network with recurrent structure for cost volume
regularization, which allows high-resolution reconstruction and finer
hypothetical plane sweep. The proposed network is trained end-to-end and
achieves excellent performance on various datasets. It ranks $1^{st}$ among all
submissions on Tanks and Temples benchmark and achieves competitive results on
DTU dataset, which exhibits strong generalizability and robustness.
Implementation of our method is available at
this https URL

本文介绍了一种利用长短时记忆的新型递归多视角立体视觉网络，该网络基于自适应聚合，并提出了协同作用的自适应聚合模块，有效改善了局部细节和大低纹理表面的性能，并且在各种数据集上取得了出色的表现。

AA-RMVSNet：自适应聚合循环多视图立体网络

AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network

Deep clustering is the first method to handle general audio separation
scenarios with multiple sources of the same type and an arbitrary number of
sources, performing impressively in speaker-independent speech separation
tasks. However, little is known about its effectiveness in other challenging
situations such as music source separation. Contrary to conventional networks
that directly estimate the source signals, deep clustering generates an
embedding for each time-frequency bin, and separates sources by clustering the
bins in the embedding space. We show that deep clustering outperforms
conventional networks on a singing voice separation task, in both matched and
mismatched conditions, even though conventional networks have the advantage of
end-to-end training for best signal approximation, presumably because its more
flexible objective engenders better regularization. Since the strengths of deep
clustering and conventional network architectures appear complementary, we
explore combining them in a single hybrid network trained via an approach akin
to multi-task learning. Remarkably, the combination significantly outperforms
either of its components.

本文探讨了 Deep clustering 在音频和音乐源分离任务中的表现，发现其优于传统网络，同时也成功地将其与传统网络可拼接在一起，构建出一个更为强大的混合网络。

深度聚类和传统网络用于音乐分离：攜手更強大

Deep Clustering and Conventional Networks for Music Separation: Stronger  Together

Research on automatically geolocating social media users has conventionally
been based on the text content of posts from a given user or the social network
of the user, with very little crossover between the two, and no bench-marking
of the two approaches over compara- ble datasets. We bring the two threads of
research together in first proposing a text-based method based on adaptive
grids, followed by a hybrid network- and text-based method. Evaluating over
three Twitter datasets, we show that the empirical difference between text- and
network-based methods is not great, and that hybridisation of the two is
superior to the component methods, especially in contexts where the user graph
is not well connected. We achieve state-of-the-art results on all three
datasets.

提出了一种基于自适应网格的基于文本的方法和基于混合网络 - 文本方法相结合的方法，这两种方法在三个 Twitter 数据集上都取得了最先进的结果，特别是在用户图不充分连接的情况下，混合方法表现更好。