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Latent attribute augmented network for few-shot class-incremental learning
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AI
DOI：10.1016/j.neucom.2025.131266.png)
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Abstract
En 中文
• We have developed a Latent Attribute Augmented Network (LAAN), which focuses on discriminative local visual regions to capture fine-grained characteristics. • Different from previous methods that utilize explicit semantic knowledge, we design an auxiliary memory to learn latent attribute prototypes automatically. • We have constructed a transformer-based knowledge interaction module, which enables the information fusion among the local image regions and latent attributes, thereby enhancing the model’s ability to process complex and detailed information.
Keywords:
Latent Attribute Augmented Network
fine-grained characteristics
latent attribute prototypes
knowledge interaction module
transformer-based model
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W
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