首先CLIP的zero-shot迁移能力非常强。如在ImageNet问题上，CLIP无需ImageNet标注数据训练，通过zero-shot...
CLIP在各大图像识别任务上都取得了非常先进的zero-shot识别性能, 即是仅用文本的信息（标签名）就可以...
实验结果如下图7所示,在一共对比的27个数据集中,Zero-Shot CLIP 在16个数据集上面战胜了全监督的 ResNet-50 模型。 在细粒度分类任务上,可以观察到性能上的广泛差异。在其中两个数据集 (Stanford Cars 和 Food101) 上,Zero-Shot CLIP 在 ResNet-50 特征上的表现比逻辑回归好 20% 以上,而在另外两个数据集...
2. Related Work Zero-shot learning (ZSL) has been a well studied area in recent years. Early ZSL approaches [10, 12, 2, 17, 19, 23] utilized the semantic label space for projecting the seen and unseen instances. DEVISE [10], ALE [1] and SJE [2] learned bi-linear com...
(TF-IDF)59, Word2Vec28, and Doc2Vec57. We further compared BioTranslator to the graph-based approach clusDCA53by replacing the text vectors with the ontology network vectors produced by clusDCA. We also compared our method with two multi-label zero-shot learning approaches, ML-ZSL62and ...
Our results show that using zero-shot recognition achieves significantly better than chance performance on document image classification benchmarks (49.51% accuracy on Tobacco-3482 in contrast to 10% random classifier accuracy and 39.22% on RVL-CDIP dataset in contrast to 6.25% random classifier ...
Earlier work in zero-shot learning use attributes in a two-step approach to infer unknown classes. In the computer vision context, more recent advances learn mappings from image feature space to semantic space. Other approaches learn non-linear multimodal embeddings. In the modern NLP context, ...
Huggingface does offer some nice models for few/zero-shot classification, but these are not tailored to multi-lingual approaches. Rasa NLU has a nice approach for this, but its too embedded in their codebase for easy usage outside of Rasa/chatbots. Additionally, it made sense to integrate ...
While standard image models jointly train an image feature extractor and a linear classifier to pred...
4.1 Zero-shot CLIP v.s. Linear Probe on ResNet50 CLIP的胜率在16/27，已经很强了，因为CLIP是...