  Self-supervised contrastive learning strategy has attracted remarkable
attention due to its exceptional ability in representation learning. However,
current contrastive learning tends to learn global coarse-grained
representations of the image that benefit generic object recognition, whereas
such coarse-grained features are insufficient for fine-grained visual
recognition. In this paper, we present to incorporate the subtle local
fine-grained feature learning into global self-supervised contrastive learning
through a pure self-supervised global-local fine-grained contrastive learning
framework. Specifically, a novel pretext task called Local Discrimination
(LoDisc) is proposed to explicitly supervise self-supervised model's focus
towards local pivotal regions which are captured by a simple-but-effective
location-wise mask sampling strategy. We show that Local Discrimination pretext
task can effectively enhance fine-grained clues in important local regions, and
the global-local framework further refines the fine-grained feature
representations of images. Extensive experimental results on different
fine-grained object recognition tasks demonstrate that the proposed method can
lead to a decent improvement in different evaluation settings. Meanwhile, the
proposed method is also effective in general object recognition tasks.
