表 1. CLIP 与之前的零样本迁移输图像分类工作的比较。 CLIP 极大地提高了所有三个数据集的性能。这...
Large Language Models in Time Series——the model serves general. 主要思想 LLMTIME: Forecasting with Language Models Tokenization Rescaling Sampling / Forecasting Continuous likelihoods Language models as flexible distributions Origins of Zero-Shot Performance Special Properties of LLMs Base models and fore...
CLIP has achieved impressive zero-shot performance after pretraining on a large-scale dataset consisting of paired image-text data. Previous works have utilized CLIP by incorporating manually designed visual prompts like colored circles and blur masks into the images to guide the model's attention, ...
Zero-Shot Performance Visual captioning Model:zeronlg-4langs-vc's multilingual decoder + CLIP's ViT-B-32 image encoder. DatasetLanguageTypeBLEU@1BLEU@2BLEU@3BLEU@4METEORROUGE-LCIDEr-DSPICE Flickr30KEnglishImage46.427.215.58.913.031.321.07.6 ...
However, their performance is limited due to ignoring the zero-shot and fine-grained characteristics presented in real person re-identification applications. In this paper, we investigate two consistencies across two cameras, which are cross-view support consistency and cross-view projection consistency....
Our paper provides empirical evidence showcasing the superior performance of ChatGPT-4 in comparison to both ChatGPT-3.5 and BARD in zero-shot setting throughout almost all evaluated tasks. While the superiority of GPT-4 compared to GPT-3.5 might be explained by its larger size and NLP ...
Large Language Models (LLMs) have exhibited remarkable performance on various Natural Language Processing (NLP) tasks. However, there is a current hot debate regarding their reasoning capacity. In this paper, we examine the performance of GPT-3.5, GPT-4, and BARD models, by performing a thoroug...
and without the need for domain expertise or trial and error. We show that fine-tuning the zero-shot classifier on its most confident predictions leads to significant performance gains across a wide range of text classification tasks, presumably since self-training adapts th...
Self2Self8 was the first blind zero-shot method whose performance approaches fully trained methods. Self2Self is a blind-spot method, however instead of replacing masked pixels, it ignores them altogether by using partial convolutions23,24. Self2Self also introduces the innovative step of adding...
In contrast, our model can both obtain state of the art performance on classes that have thousands of training images and obtain reasonable performance on unseen classes. This is achieved by first using outlier detection in the semantic space and then two separate recognition models. Furthermore, ...