Semantic interaction learning for fine‐grained vehicle recognition

Abstract

Abstract Fine‐grained vehicle recognition is a challenging problem due to high inter‐class confusion among vehicle models under the influence of pose and viewpoint. To effectively describe the discriminative characteristics, many approaches try to learn detailed information from an individual image. Inspired by Siamese network that addresses the case where two inputs are relatively similar, the semantic interaction learning network (SIL‐Net) is designed to discover semantic differences between two fine‐grained categories via pairwise comparison. Specifically, SIL‐Net first collecting contrastive information by learning the mutual feature of input image pair, and then compare it with individual features to generate corresponding semantic features. These features learn semantic differences from contextual comparison, this gives SIL‐Net the ability to distinguish between two confusing images via pairwise interaction. After training, SIL‐Net can adaptively learn feature priorities under the supervision of the margin ranking loss and converge quickly. SIL‐Net performs well on two public vehicle benchmarks (Stanford Cars and CompCars), showing the suitability of SIL‐Net to fine‐grained vehicle recognition.