We present a unified framework called deep dependency networks (DDNs) that
combines dependency networks and deep learning architectures for multi-label
classification, with a particular emphasis on image and video data. The primary
advantage of dependency networks is their ease of training, in contrast to
other probabilistic graphical models like Markov networks. In particular, when
combined with deep learning architectures, they provide an intuitive,
easy-to-use loss function for multi-label classification. A drawback of DDNs
compared to Markov networks is their lack of advanced inference schemes,
necessitating the use of Gibbs sampling. To address this challenge, we propose
novel inference schemes based on local search and integer linear programming
for computing the most likely assignment to the labels given observations. We
evaluate our novel methods on three video datasets (Charades, TACoS, Wetlab)
and three image datasets (MS-COCO, PASCAL VOC, NUS-WIDE), comparing their
performance with (a) basic neural architectures and (b) neural architectures
combined with Markov networks equipped with advanced inference and learning
techniques. Our results demonstrate the superiority of our new DDN methods over
the two competing approaches.

我们提出了一个统一的框架，称为深层依赖网络（DDN），它将依赖网络和深度学习架构结合起来，用于多标签分类，特别是在图像和视频数据上。

深度依赖网络和高级推理方案在多标签分类中的应用

Deep Dependency Networks and Advanced Inference Schemes for Multi-Label  Classification

We consider a class of learning problems that involve a structured
sparsity-inducing norm defined as the sum of $\ell_\infty$-norms over groups of
variables. Whereas a lot of effort has been put in developing fast optimization
methods when the groups are disjoint or embedded in a specific hierarchical
structure, we address here the case of general overlapping groups. To this end,
we show that the corresponding optimization problem is related to network flow
optimization. More precisely, the proximal problem associated with the norm we
consider is dual to a quadratic min-cost flow problem. We propose an efficient
procedure which computes its solution exactly in polynomial time. Our algorithm
scales up to millions of variables, and opens up a whole new range of
applications for structured sparse models. We present several experiments on
image and video data, demonstrating the applicability and scalability of our
approach for various problems.

本文研究了一个结构化稀疏正则化学习问题，提出了一种基于网络流优化的高效算法，在多个领域展示出了它的可行性和可扩展性。