Revisiting Fine-Grained Image Analysis by Semantic-Part Alignment

Fine-grained image analysis is widely recognized as highly challenging, since distinguishing individual differences within a certain category, species, or type often depends on tiny, subtle patterns. However, learning fine-grained semantic categories from these subtle part patterns is inherently fragile, as they can easily be overwhelmed by the dominant patterns resting in the coarse-category information. Therefore, how to enhance the relation between the fine-grained semantics and these subtle patterns is the key. To push this frontier, a novel semantic-part alignment (SPA) learning scheme is proposed in this paper. Its general idea is to firstly measure the relevance of each part to the fine-grained semantics, and then regularize the fine-grained visual representation learning. Specifically, it consists of three key components, namely, joint semantic-part modeling, semantic-part set modeling, and optimal semantic-part transport. The joint semantic-part modeling associates each part in an image with the fine-grained semantics in a latent space. Then, the optimal semantic-part transport component is devised to enhance the relation between fine-grained semantic embeddings and the discriminative part embeddings. Notably, the proposed SPA is plug-in-and-play, easy-to-implement, and insensitive to the latent embedding dimension and loss weight. Experiments show the proposed method can substantially boost performance on multiple fine-grained image analysis tasks across various baselines.