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Found 3 papers, 1 papers with code
Data Efficient Language-supervised Zero-shot Recognition with Optimal Transport Distillation
Traditional computer vision models are trained to predict a fixed set of predefined categories.
Data-Efficient Language-Supervised Zero-Shot Learning with Self-Distillation
Our model transfers knowledge from pretrained image and sentence encoders and achieves strong performance with only 3M image text pairs, 133x smaller than CLIP.
Pay Attention to Convolution Filters: Towards Fast and Accurate Fine-Grained Transfer Learning
We propose an efficient transfer learning method for adapting ImageNet pre-trained Convolutional Neural Network (CNN) to fine-grained image classification task.
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