  Fine-grained classification is challenging due to the difficulty of finding
discriminative features. Finding those subtle traits that fully characterize
the object is not straightforward. To handle this circumstance, we propose a
novel self-supervision mechanism to effectively localize informative regions
without the need of bounding-box/part annotations. Our model, termed NTS-Net
for Navigator-Teacher-Scrutinizer Network, consists of a Navigator agent, a
Teacher agent and a Scrutinizer agent. In consideration of intrinsic
consistency between informativeness of the regions and their probability being
ground-truth class, we design a novel training paradigm, which enables
Navigator to detect most informative regions under the guidance from Teacher.
After that, the Scrutinizer scrutinizes the proposed regions from Navigator and
makes predictions. Our model can be viewed as a multi-agent cooperation,
wherein agents benefit from each other, and make progress together. NTS-Net can
be trained end-to-end, while provides accurate fine-grained classification
predictions as well as highly informative regions during inference. We achieve
state-of-the-art performance in extensive benchmark datasets.
