Dual-Dependency Attention Transformer for Fine-Grained Visual Classification

Dual-Dependency Attention Transformer for Fine-Grained Visual Classification

Blog Article

Visual transformers (ViTs) are widely used in various visual tasks, such as fine-grained visual classification (FGVC).However, the self-attention mechanism, which is the core module of old taylor whiskey 1933 price visual transformers, leads to quadratic computational and memory complexity.The sparse-attention and local-attention approaches currently used by most researchers are not suitable for FGVC tasks.These tasks require dense feature extraction and global dependency modeling.

To address this challenge, we propose a dual-dependency attention transformer model.It decouples global token interactions into two paths.The first is a position-dependency attention pathway based on the intersection of two types of grouped attention.The second is a semantic dependency attention pathway based on dynamic central aggregation.

This approach enhances the high-quality semantic modeling of discriminative cues while reducing the computational cost to linear computational complexity.In addition, we develop discriminative enhancement strategies.These strategies increase the sensitivity of high-confidence discriminative cue tracking with a knowledge-based representation approach.Experiments on three datasets, quest fryer NABIRDS, CUB, and DOGS, show that the method is suitable for fine-grained image classification.

It finds a balance between computational cost and performance.