Papers › Learning Attentive Pairwise Interaction for Fine-Grained Classification
Learning Attentive Pairwise Interaction for Fine-Grained Classification
Peiqin Zhuang, Yali Wang, Yu Qiao
Fine-grained classification is a challenging problem, due to subtle differences among highly-confused categories. Most approaches address this difficulty by learning discriminative representation of individual input image. On the other hand, humans can effectively identify contrastive clues by comparing image pairs. Inspired by this fact, this paper proposes a simple but effective Attentive Pairwise Interaction Network (API-Net), which can progressively recognize a pair of fine-grained images by interaction. Specifically, API-Net first learns a mutual feature vector to capture semantic differences in the input pair. It then compares this mutual vector with individual vectors to generate gates for each input image. These distinct gate vectors inherit mutual context on semantic differences, which allow API-Net to attentively capture contrastive clues by pairwise interaction between two images. Additionally, we train API-Net in an end-to-end manner with a score ranking regularization, which can further generalize API-Net by taking feature priorities into account. We conduct extensive experiments on five popular benchmarks in fine-grained classification. API-Net outperforms the recent SOTA methods, i.e., CUB-200-2011 (90.0%), Aircraft(93.9%), Stanford Cars (95.3%), Stanford Dogs (90.3%), and NABirds (88.1%).
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fine-Grained Image Classification | FGVC Aircraft | API-Net | Accuracy | 93.9% | #16 of 57 | Archive leaderboard | report |
| Fine-Grained Image Classification | NABirds | API-Net | Accuracy | 88.1% | #24 of 30 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Cars | API-Net | Accuracy | 95.3% | #17 of 83 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Dogs | API-Net | Accuracy | 90.3% | #17 of 24 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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