Abstract 在小样本学习中(Few-shot Learning, FSL)中,有通过利用额外的语义信息,如类名的文本Embedding,通过将语义原型与视觉原型相结合来解决样本稀少的问题。但这种方法可能会遇到稀有样本中学到噪声特征导致收益有限。在这篇论文,作者提出了一种用于少样本学习的语义提示(Semantic Prompt, SP)方法,不同于简单地利用...
IntroductionAsthelabeledimagesofnovelclassesarescarce,astraightforwardalternativeistouseauxiliaryinformationfromothermodalities,e.g.naturallanguage,toassistinlearningnewconcepts,whichhasbeenextensivelystudiedinzero-shotlearning.Thesemethodsusuallydirectlyusetextualembeddingsastheimageclassifiersfornovelclasses.Followingthisidea...
The challenges of collecting and annotating high-quality multi-modal data have underscored the significance of few-shot learning. In this paper, we focus on two critical tasks under this context: few-shot multi-modal sarcasm detection (MSD) and multi-modal sentiment analysis (MSA). To address ...
今天看到了MSRA的一篇用MaskFormer+CLIP的text encoder去做zero-shot语义分割,感觉还挺有意思的,就决定写一下我乎第一篇论文笔记吧。这里主要讲一下这篇论文的大致想法和框架,一些细节性的东西(如prompt templ…
Prompt-tuning methods for Continual Learning (CL) freeze a large pre-trained model and train a few parameter vectors termed prompts. Most of these methods organize these vectors in a poolof key-value pairs and use the input image as query to retrievethe prompts (values). However, as keys ...
While this is ok for getting started, it's not recommended for more complex scenarios since the AI may not have enough training data to generate the correct result.To add examples, we can use few-shot prompting. With few-shot prompting, we provide the AI with a few examples of what we...
An introduction to Semantic Kernel, who is it for, why use it, key components and creating a simple agent. 1– Working with Inline Prompt Functions Learn about leveraging prompt functions to ensure predictability, refine LLM prompt output, few shot prompting, and handling unexpected ...
is introduced according to Euler’s formula for computing the real and imaginary parts of complex word embedding. The weights of the phase embedding layer and amplitude embedding layer are trainable for learning the most suitable amplitudes and phases automatically by the training data of a specific...
Few-Shot Novel Concept Learning for Semantic Parsing [paper] Soham Dan, Osbert Bastani, Dan Roth. EMNLP-finding-2021. Weakly Supervised Semantic Parsing by Learning from Mistakes [paper] Jiaqi Guo, Jian-Guang Lou, Ting Liu, Dongmei Zhang. EMNLP-finding-2021. Translate & Fill: Improving Zero...
Few-Shot Meta-Learning (FSML) is a machine learning technique that uses a minimal number of labeled samples per class to guarantee that a pre-trained model generalizes across new types of data (that the pre-trained model has not seen in training). It is unique compared to traditional supervi...