论文标题
关系代理：新兴关系作为细粒度的歧视者
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
论文作者
论文摘要
基本共享相同零件的细粒类别不能仅根据部分信息来歧视，因为它们在本地部分与对象的整体全局结构相关的方式大多有所不同。我们提出了关系代理，这是一种新颖的方法，利用对象的全球和本地观点之间的关系信息来编码其语义标签。从对细粒类别之间的区分性概念进行严格形式化开始，我们证明了模型必须满足的必要条件，以便在细粒度的环境中学习潜在的决策界限。我们根据我们的理论发现设计关系代理，并在七个具有挑战性的细粒基准数据集上对其进行评估，并在所有这些基准数据集中实现最先进的结果，超过了所有现有作品的性能，在某些情况下，利润率超过4％。我们还在实验中验证了我们的理论关于细粒度的区分性，并在多个基准中获得一致的结果。实施可从https://github.com/abhrac/relational-proxies获得。
Fine-grained categories that largely share the same set of parts cannot be discriminated based on part information alone, as they mostly differ in the way the local parts relate to the overall global structure of the object. We propose Relational Proxies, a novel approach that leverages the relational information between the global and local views of an object for encoding its semantic label. Starting with a rigorous formalization of the notion of distinguishability between fine-grained categories, we prove the necessary and sufficient conditions that a model must satisfy in order to learn the underlying decision boundaries in the fine-grained setting. We design Relational Proxies based on our theoretical findings and evaluate it on seven challenging fine-grained benchmark datasets and achieve state-of-the-art results on all of them, surpassing the performance of all existing works with a margin exceeding 4% in some cases. We also experimentally validate our theory on fine-grained distinguishability and obtain consistent results across multiple benchmarks. Implementation is available at https://github.com/abhrac/relational-proxies.
