A Contrastive Learning Framework Empowered by Attention-based Feature Adaptation for Street-View Image Classification

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• CLIP-MHAdapter achieves superior or competitive accuracy across eight attribute classification tasks on the Global StreetScapes dataset.result 95%
• CLIP-MHAdapter maintains low computational cost.technical 90%
• CLIP-MHAdapter is a variant of the lightweight CLIP adaptation paradigm.method 90%
• CLIP-MHAdapter appends a bottleneck MLP equipped with multi-head self-attention operating on patch tokens to model inter-patch dependencies.method 95%
• The proposed method can replace existing computationally expensive image classification techniques for street-view data.market 80%
• Model performance might vary with non-standardized street-view images not covered in the training dataset.limitation 90%
• The proposed framework has approximately 1.4 million trainable parameters.technical 95%

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