This paper first proposes a method of formulating model interpretability in
visual understanding tasks based on the idea of unfolding latent structures. It
then presents a case study in object detection using popular two-stage
region-based convolutional network (i.e., R-CNN) detection systems. We focus on
weakly-supervised extractive rationale generation, that is learning to unfold
latent discriminative part configurations of object instances automatically and
simultaneously in detection without using any supervision for part
configurations. We utilize a top-down hierarchical and compositional grammar
model embedded in a directed acyclic AND-OR Graph (AOG) to explore and unfold
the space of latent part configurations of regions of interest (RoIs). We
propose an AOGParsing operator to substitute the RoIPooling operator widely
used in R-CNN. In detection, a bounding box is interpreted by the best parse
tree derived from the AOG on-the-fly, which is treated as the qualitatively
extractive rationale generated for interpreting detection. We propose a
folding-unfolding method to train the AOG and convolutional networks
end-to-end. In experiments, we build on R-FCN and test our method on the PASCAL
VOC 2007 and 2012 datasets. We show that the method can unfold promising latent
structures without hurting the performance.

本文提出一种基于展开潜在结构的模型可解释性解释方法并以物体检测为案例运用了有向无环图模型探索 ROI 区域中潜在的部件结构并自动生成部件组合，最后在 PASCAL VOC 2007 和 2012 数据集上进行验证，结果表明其方法能够不影响性能的情况下展现有希望的潜在结构。

通过展开潜在结构实现可解释的 R-CNN

Towards Interpretable R-CNN by Unfolding Latent Structures

This paper proposes a reconfigurable model to recognize and detect multiclass
(or multiview) objects with large variation in appearance. Compared with well
acknowledged hierarchical models, we study two advanced capabilities in
hierarchy for object modeling: (i) "switch" variables(i.e. or-nodes) for
specifying alternative compositions, and (ii) making local classifiers (i.e.
leaf-nodes) shared among different classes. These capabilities enable us to
account well for structural variabilities while preserving the model compact.
Our model, in the form of an And-Or Graph, comprises four layers: a batch of
leaf-nodes with collaborative edges in bottom for localizing object parts; the
or-nodes over bottom to activate their children leaf-nodes; the and-nodes to
classify objects as a whole; one root-node on the top for switching multiclass
classification, which is also an or-node. For model training, we present an
EM-type algorithm, namely dynamical structural optimization (DSO), to
iteratively determine the structural configuration, (e.g., leaf-node generation
associated with their parent or-nodes and shared across other classes), along
with optimizing multi-layer parameters. The proposed method is valid on
challenging databases, e.g., PASCAL VOC 2007 and UIUC-People, and it achieves
state-of-the-arts performance.

本文提出一种可重构模型，用于识别和检测外观差异较大的多类（或多视）物体，该模型采用 And-Or 图结构，具备分层建模、结构可变性处理等先进能力，并提出了一种 EM 类型算法，在 PASCAL VOC 2007 和 UIUC-People 数据库等复杂环境中实现了最新的性能表现。

将结构替代和共享融入层级结构中，用于多类别目标识别和检测

Incorporating Structural Alternatives and Sharing into Hierarchy for  Multiclass Object Recognition and Detection

This paper studies a novel discriminative part-based model to represent and
recognize object shapes with an "And-Or graph". We define this model consisting
of three layers: the leaf-nodes with collaborative edges for localizing local
parts, the or-nodes specifying the switch of leaf-nodes, and the root-node
encoding the global verification. A discriminative learning algorithm, extended
from the CCCP [23], is proposed to train the model in a dynamical manner: the
model structure (e.g., the configuration of the leaf-nodes associated with the
or-nodes) is automatically determined with optimizing the multi-layer
parameters during the iteration. The advantages of our method are two-fold. (i)
The And-Or graph model enables us to handle well large intra-class variance and
background clutters for object shape detection from images. (ii) The proposed
learning algorithm is able to obtain the And-Or graph representation without
requiring elaborate supervision and initialization. We validate the proposed
method on several challenging databases (e.g., INRIA-Horse, ETHZ-Shape, and
UIUC-People), and it outperforms the state-of-the-arts approaches.

本文介绍了一种新的基于判别式局部模型的 And-Or 图方法来表示和识别目标形状，该方法克服了困难的背景环境和大量的形态变化，并且不需要太多的监督和初始化。