Hierarchical multi-granularity classification (HMC) assigns hierarchical
multi-granularity labels to each object and focuses on encoding the label
hierarchy, e.g., ["Albatross", "Laysan Albatross"] from coarse-to-fine levels.
However, the definition of what is fine-grained is subjective, and the image
quality may affect the identification. Thus, samples could be observed at any
level of the hierarchy, e.g., ["Albatross"] or ["Albatross", "Laysan
Albatross"], and examples discerned at coarse categories are often neglected in
the conventional setting of HMC. In this paper, we study the HMC problem in
which objects are labeled at any level of the hierarchy. The essential designs
of the proposed method are derived from two motivations: (1) learning with
objects labeled at various levels should transfer hierarchical knowledge
between levels; (2) lower-level classes should inherit attributes related to
upper-level superclasses. The proposed combinatorial loss maximizes the
marginal probability of the observed ground truth label by aggregating
information from related labels defined in the tree hierarchy. If the observed
label is at the leaf level, the combinatorial loss further imposes the
multi-class cross-entropy loss to increase the weight of fine-grained
classification loss. Considering the hierarchical feature interaction, we
propose a hierarchical residual network (HRN), in which granularity-specific
features from parent levels acting as residual connections are added to
features of children levels. Experiments on three commonly used datasets
demonstrate the effectiveness of our approach compared to the state-of-the-art
HMC approaches and fine-grained visual classification (FGVC) methods exploiting
the label hierarchy.

该论文研究了层次多粒度分类问题，基于所提出的组合损失以及继承父级别特征的方法，设计了一种基于层次残差网络的深度神经网络结构，展示了该方法有效性在通常数据集上优于目前最先进的方法及精细分类方法利用标签层次结构。

标签关系图增强的分层残差网络用于分层多粒度分类

Label Relation Graphs Enhanced Hierarchical Residual Network for  Hierarchical Multi-Granularity Classification

Crowd counting has recently attracted increasing interest in computer vision
but remains a challenging problem. In this paper, we propose a trellis
encoder-decoder network (TEDnet) for crowd counting, which focuses on
generating high-quality density estimation maps. The major contributions are
four-fold. First, we develop a new trellis architecture that incorporates
multiple decoding paths to hierarchically aggregate features at different
encoding stages, which can handle large variations of objects. Second, we
design dense skip connections interleaved across paths to facilitate sufficient
multi-scale feature fusions and to absorb the supervision information. Third,
we propose a new combinatorial loss to enforce local coherence and spatial
correlation in density maps. By distributedly imposing this combinatorial loss
on intermediate outputs, gradient vanishing can be largely alleviated for
better back-propagation and faster convergence. Finally, our TEDnet achieves
new state-of-the art performance on four benchmarks, with an improvement up to
14% in terms of MAE.

研究了人群计数，在此提出了一种基于 trellis 编码器解码器网络 (TEDnet) 的方法，集中在生成高质量的密度估计图，并在四个基准测试中取得了最新的最佳表现。