Whether what you see in Figure 1 is a "flamingo" or a "bird", is the question
we ask in this paper. While fine-grained visual classification (FGVC) strives
to arrive at the former, for the majority of us non-experts just "bird" would
probably suffice. The real question is therefore -- how can we tailor for
different fine-grained definitions under divergent levels of expertise. For
that, we re-envisage the traditional setting of FGVC, from single-label
classification, to that of top-down traversal of a pre-defined coarse-to-fine
label hierarchy -- so that our answer becomes
"bird"-->"Phoenicopteriformes"-->"Phoenicopteridae"-->"flamingo". To approach
this new problem, we first conduct a comprehensive human study where we confirm
that most participants prefer multi-granularity labels, regardless whether they
consider themselves experts. We then discover the key intuition that:
coarse-level label prediction exacerbates fine-grained feature learning, yet
fine-level feature betters the learning of coarse-level classifier. This
discovery enables us to design a very simple albeit surprisingly effective
solution to our new problem, where we (i) leverage level-specific
classification heads to disentangle coarse-level features with fine-grained
ones, and (ii) allow finer-grained features to participate in coarser-grained
label predictions, which in turn helps with better disentanglement. Experiments
show that our method achieves superior performance in the new FGVC setting, and
performs better than state-of-the-art on traditional single-label FGVC problem
as well. Thanks to its simplicity, our method can be easily implemented on top
of any existing FGVC frameworks and is parameter-free.

通过构建分类标签层次结构、引入多粒度标签、优化特征学习和分类方法，本文在细粒度图像分类中达到优秀表现，并且比现有的单标签细粒度图像分类表现更佳。