ArticleSensors (Basel, Switzerland)2023
Multi-Scale Feature Fusion of Covariance Pooling Networks for Fine-Grained Visual Recognition.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An integrated framework for multi-feature fusion and intelligent recognition of design elements: Challenges and solutions.PloS one · 2025Article
- Multi-FusNet: fusion mapping of features for fine-grained image retrieval networks.PeerJ. Computer science · 2024Article
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Authors and funding
3 authors.
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Abstract
Multi-scale feature fusion techniques and covariance pooling have been shown to have positive implications for completing computer vision tasks, including fine-grained image classification. However, existing algorithms that use multi-scale feature fusion techniques for fine-grained classification tend to consider only the first-order information of the features, failing to capture more discriminative features. Likewise, existing fine-grained classification algorithms using covariance pooling tend to focus only on the correlation between feature channels without considering how to better capture the global and local features of the image. Therefore, this paper proposes a multi-scale covariance pooling network (MSCPN) that can capture and better fuse features at different scales to generate more representative features. Experimental results on the CUB200 and MIT indoor67 datasets achieve state-of-the-art performance (CUB200: 94.31% and MIT indoor67: 92.11%).
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Registered trials
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