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A collaborative gated attention network for fine-grained visual classification
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AI
DOI：10.1016/j.displa.2023.102468.png)
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摘要
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Fine-grained image classification aims at subdividing large coarse-grained categories into finer-grained subcate-gories. Most existing fine-grained research methods use a single attention mechanism or multiple sub-networks to zoom in and find distinguishable local feature regions. These models seldom explore the intrinsic connections between cross-layer features with similar semantic features. This tends to show erratic performance in images with complex backgrounds. To this end, we propose a feature-semantic fusion module to enhance the diversity of global feature information. Second, we employ cross-layer spatial attention and channel attention modules, which can accurately locate local key regions of images. Finally, we propose a cross-gate attention module that can find rich discriminative features from key object regions of images to guide the final classification. Experiments show that the proposed model performs well on three datasets: CUB-200-2011, Stanford cars, and FGVC aircraft.
Keyword:
Feature fusion
Dual attention
Gating mechanism
Global features
Local features
期刊
IF:
3.4
论文数:
2.3K
被引数:
3.2K
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分享收藏MODELING URBAN DYNAMICS USING RANDOM FOREST: IMPLEMENTING ROC AND TOC FOR MODEL EVALUATION.png)
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesIF0
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