Tag: image-classification

• AutoCLIP Auto-tuning Zero-Shot Classifiers for Vision-Language Models (29 Jul 2023)
  

  This is my reading note for AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models. This paper proposes a method to use clip for zero shot image classification, to do that, it first generates several prompt to convert class label to text embedding by average. Then the image is processed by visual encoder. The label of image is the one has slowest distance between label embody and image embedding. This paper propose to use soft Max instead of average for label embedding.

• Swin Transformer (11 Apr 2021)
  

  ViT provides the possibilities of using transformers along as a backbone for vision tasks. However, due to transformer conduct global self attention, where the relationships of a token and all other tokens are computed, its complexity grows exponentially with image resolution. This makes it inefficient for image segmentation or semantic segmentation task. To this end, twin transformer is proposed in Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, which addresses the computation issue by conducting self attention in a local window and has multi-layers for windows at different resolution.