mlsanityData to 13 Oct 2026

Progressive Co-Attention Network for Fine-grained Visual Classification

arXiv 2101.08527 ↗First posted 21 Jan 2021cs.CVcited by 5

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Abstract (arXiv)

Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category. It is a challenging task due to the inherently subtle variations among highly-confused categories. Most existing methods only take an individual image as input, which may limit the ability of models to recognize contrastive clues from different images. In this paper, we propose an effective method called progressive co-attention network (PCA-Net) to tackle this problem. Specifically, we calculate the channel-wise similarity by encouraging interaction between the feature channels within same-category image pairs to capture the common discriminative features. Considering that complementary information is also crucial for recognition, we erase the prominent areas enhanced by the channel interaction to force the network to focus on other discriminative regions. The proposed model has achieved competitive results on three fine-grained visual classification benchmark datasets: CUB-200-2011, Stanford Cars, and FGVC Aircraft.

Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 45 of its 66 references that are in the map. First posted on arXiv 21 Jan 2021.

Builds on

45 of 66 references.

References · 45

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33 more
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Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 2.

5 papers.

  1. 2024Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretationscited by 33
  2. 2023Fine-grained Visual Classification with High-temperature Refinement and Background Suppressioncited by 22
  3. 2021Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classificationcited by 1
  4. 2021Weakly Supervised Recovery of Semantic Attributescited by 0
  5. 2024Extremely Fine-Grained Visual Classification over Resembling Glyphs in the Wildcited by 0