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.

通过自监督局部微观对比学习任务，将细微的局部特征学习融合到全局自监督对比学习中，形成一个纯自监督的全局 - 局部微细对比学习框架，从而有效提高了细粒度图像特征表示的品质。

LoDisc: 全局 - 本地辨别特征的自监督细粒度视觉识别学习

LoDisc: Learning Global-Local Discriminative Features for  Self-Supervised Fine-Grained Visual Recognition

Aiming at expanding few-shot relations' coverage in knowledge graphs (KGs),
few-shot knowledge graph completion (FKGC) has recently gained more research
interests. Some existing models employ a few-shot relation's multi-hop neighbor
information to enhance its semantic representation. However, noise neighbor
information might be amplified when the neighborhood is excessively sparse and
no neighbor is available to represent the few-shot relation. Moreover, modeling
and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and
many-to-many (N-N) by previous knowledge graph completion approaches requires
high model complexity and a large amount of training instances. Thus, inferring
complex relations in the few-shot scenario is difficult for FKGC models due to
limited training instances. In this paper, we propose a few-shot relational
learning with global-local framework to address the above issues. At the global
stage, a novel gated and attentive neighbor aggregator is built for accurately
integrating the semantics of a few-shot relation's neighborhood, which helps
filtering the noise neighbors even if a KG contains extremely sparse
neighborhoods. For the local stage, a meta-learning based TransH (MTransH)
method is designed to model complex relations and train our model in a few-shot
learning fashion. Extensive experiments show that our model outperforms the
state-of-the-art FKGC approaches on the frequently-used benchmark datasets
NELL-One and Wiki-One. Compared with the strong baseline model MetaR, our model
achieves 5-shot FKGC performance improvements of 8.0% on NELL-One and 2.8% on
Wiki-One by the metric Hits@10.

本文提出了一个使用全局 - 局部框架的少样本关系学习方法，在全局阶段中，引入了一种新颖的门控和注意力邻居聚合器用于准确地集成少样本关系邻居的语义信息，并在局部阶段中使用基于元学习的 TransH 方法来模拟和推断复杂的关系，实验证明该模型在常用基准数据集 NELL-One 和 Wiki-One 上比最先进的 FKGC 方法表现更好，具有更好的 5-shot FKGC 性能。