  Fine-grained object categorization aims for distinguishing objects of
subordinate categories that belong to the same entry-level object category. The
task is challenging due to the facts that (1) training images with ground-truth
labels are difficult to obtain, and (2) variations among different subordinate
categories are subtle. It is well established that characterizing features of
different subordinate categories are located on local parts of object
instances. In fact, careful part annotations are available in many fine-grained
categorization datasets. However, manually annotating object parts requires
expertise, which is also difficult to generalize to new fine-grained
categorization tasks. In this work, we propose a Weakly Supervised Part
Detection Network (PartNet) that is able to detect discriminative local parts
for use of fine-grained categorization. A vanilla PartNet builds on top of a
base subnetwork two parallel streams of upper network layers, which
respectively compute scores of classification probabilities (over subordinate
categories) and detection probabilities (over a specified number of
discriminative part detectors) for local regions of interest (RoIs). The
image-level prediction is obtained by aggregating element-wise products of
these region-level probabilities. To generate a diverse set of RoIs as inputs
of PartNet, we propose a simple Discretized Part Proposals module (DPP) that
directly targets for proposing candidates of discriminative local parts, with
no bridging via object-level proposals. Experiments on the benchmark
CUB-200-2011 and Oxford Flower 102 datasets show the efficacy of our proposed
method for both discriminative part detection and fine-grained categorization.
In particular, we achieve the new state-of-the-art performance on CUB-200-2011
dataset when ground-truth part annotations are not available.
