Label-Embedding for Image Classification. Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce. We propose to view attri... Zeynep,Akata,Florent,... - 《IEEE Transactions on Pattern Analysis & Machine Intelligence》 被引量...
In image classification, each class of a set of classes is embedded in an attribute space where each dimension of the attribute space corresponds to a class attribute. The embedding generates a class attribute vector for each class of the set of classes. A set of parameters of a prediction ...
论文名字:Multi-TaskLabelEmbeddingfor Text Classification 动机文本分类中的多任务学习利用相关任务之间的隐式关联来提取共同特征并获得性能增益。然而,以往的研究大多将每个任务的标签视为独立的、无意义的one-hot向量,导致潜在信息的丢失,使得这些模型很难联合学习三个或更多个任务。 预学习概念文本分类是一种常见的自...
CNLE: Co-attention Network with Label Embedding for Text Classification Created by Minqian Liu, Lizhao Liu and Junyi Cao from South China University of Technology. This repository contains the official PyTorch-implementation of ourNeurocomputing 2022 paperCo-attention network with label embedding for ...
Full size image Performance comparison among different predictors for therapeutic peptide function prediction Most of the existing methods only predict some specific therapeutic peptide functions and treat this task as binary classification problem. In contrast, TPpred-LE is the only method for comprehensiv...
Multi-label learning refers to methods for learning a classification function that predicts a set of relevant labels for an instance. Label embedding seeks... S Park,S Choi - 《Pattern Recognition Letters》 被引量: 9发表: 2013年 Multi-Label Image Recognition with Joint Class-Aware Map Disentang...
[ACLW'24] LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition arxiv.org/abs/2305.04536 Topics multi-label-image-classification prompt-tuning long-tailed-learning vision-language-model Resources Readme License Apache-2.0 license Activity Stars ...
A global geometric framework for nonlinear dimensionality reduction[J].Science, 2000, 290(5500):2319-2323. [17] HUANG Hong, HUANG Yunbiao. Improved discriminant sparsity neighborhood preserving embedding for hyperspectral image classification[J].Neurocomputing, 2014, 136(2014):224-234. [18] HE ...
For addressing the data imbalance problem, we used a weight sharing classification layer to classify the labels according to the relevance of the fact vector to the law article vector of the vector space. We used frequently occurring articles of various laws to train a transfer learning model ...
We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function which measures the compatibility between an image and a label embedding. The parameters of this function are learned on a ...