在VQA、OKVQA、miniImageNet等多个数据集验证了迁移学习的效果,在few-shot场景下验证了prefix对模型的提升。通过将图片作为prefix,从而将文本中的静态prefix转化为动态prefix,能更好的适应不同的任务。但是,由于Visiual Encoder和文本的处理比较简单,模型效果离SOTA有一定的距离。
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论文题目:Flamingo: a Visual Language Model for Few-Shot Learning 论文地址:http://arxiv.org/abs/2204.14198 * 本视频旨在传递一篇论文的存在推荐感兴趣的您阅读,并不是详细介绍,受up能力限制经常出现中英混杂,散装英语等现象,请见谅。如论文报道出了偏差,欢迎各位怒斥。 ** 新论文推荐,过往论文查找,欢迎编辑...
原文:Atlas: Few-shot Learning with Retrieval Augmented Language Models 作者: Gautier Izacard∗∗,♦,♣,♥ Patrick Lewis∗,♦ Maria Lomeli♦ Lucas Hosseini♦ Fabio Petroni♦ Timo Schick♦ Jane Dwi…
Atlas: Few-shot Learning with Retrieval Augmented Language Models Aug 2022 Gautier Izacard, Patrick Lewis等 [Meta AI Research等] https://arxiv.org/abs/2208.03299 https://github.com/facebookresearch/atlas 大型语言模型在广泛的任务中显示出令人印象深刻的few-shot结果。然而,当知识是这些结果的关键时,...
In light of the above-mentioned facts, in the present work, we have proposed an approach to determine early detection with the goal of early diagnosis of suicidal behavior through text posted on social media via supervised learning using a few-shot learning process. Therefore, to detect suicidal...
[1] Timo Schick and Hinrich Schütze. 2021b. It’s not just size that matters: Small language models are also few-shot learners. pages 2339–2352. [2] Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021. True few-shot learning with language models. CoRR, abs/2105.11447. ...
内容提示: Continual Training of Language Models for Few-Shot LearningZixuan Ke 1 , Haowei Lin 2 , Yijia Shao 2 , Hu Xu 1 , Lei Shu 1∗ and Bing Liu 11 Department of Computer Science, University of Illinois at Chicago2 Wangxuan Institute of Computer Technology, Peking University1 {zke...
By presenting a broad characterization of GPT-3’s strengths and weaknesses, including these limitations, we hope to stimulate study of few-shot learning in language models and draw attention to where progress is most needed. A heuristic sense of the overall results can be seen in Figure 1.3...
By presenting a broad characterization of GPT-3’s strengths and weaknesses, including these limitations, we hope to stimulate study of few-shot learning in language models and draw attention to where progress is most needed. A heuristic sense of the overall results can be seen in Figure 1.3,...