Zero-shot learning (ZSL) aims to recognize unseen object classes without any training samples, which can be regarded as a form of transfer learning from seen classes to unseen ones. This is made possible by learning a projection between a feature space and a semantic space (e.g.

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SpeAr, which can further alleviate the bias problem.

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In the supplementary material, we first introduce technical details of the "frozen CLIP" approaches in Sec. 1. Then the dataset settings are shown in Sec. 2. Figure 1 presents an overview of the "frozen CLIP" approach. It's worth noting that all sub-images are resized to Figure 2: Comparison among three merge operations. Pascal-VOC, COCO-Stuff and ADE20K, to evaluate the performance of MAFT. Pascal-VOC: There are 10582 images for training and 1,449 images for testing. ADE20K: ADE20K contains 25k images for training and 2k images for validation. Pascal-Context is an extensive dataset of Pascal-VOC 2010.

Recently, pre-trained vision-language models have been increasingly used to tackle the challenging zero-shot segmentation task.

First, our generative model synthesizes inter-class discrimination visual features via59 a classification loss constraint, which makes sure that synthetic visual features are discriminative60 enoughamongdifferentclasses.

This paper aims to tackle the challenging problem of one-shot object detection.