Label-Embedding for Image Classification. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We ... Zeynep,Akata,Flor
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 ...
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...
The International Classification of Diseases, 9th Revision (ICD-9) coding system provides a standardized representation of diseases and surgeries, forming a hierarchical structure with unique codes assigned to each condition. Effectively utilizing this structured data is essential for developing accurate pred...
Correspondence toFan Min. Ethics declarations Conflicts of interest The authors have no relevant financial or non-financial interests to disclose. Ethical and informed consent for data used The data used in this study were obtained from publicly available datasets as described in Table2.The original ...
Bruzzone L, Marconcini M (2010) Domain adaptation problems: a DASVM classification technique and a circular validation strategy. IEEE Trans Pattern Anal 32(5):770–787 Article Google Scholar Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural...
In this paper, with the introduction of a label correction mechanism to identify missing labels, we first elegantly generate positives and negatives for individual semantic labels of an anchor image, then define a unique contrastive loss for multi-label image classification with missing labels (CLML...
Multi-label classification has been successfully applied to image annotation, information retrieval, text categorization, etc. When the number of classes increases significantly, the traditional multi-label learning models will become computationally impractical. Label space dimension reduction (LSDR) is ...
feature extraction,geophysical image processing,hyperspectral imaging,image classification,image representation,learning (artificial intelligence),solid modelling,statistical analysisCurrently, Hyperspectral signal processing is a crucial area of research. Respectively, various techniques have been investigated to ...
Multi-label image and video classification are fundamental yet challenging tasks in computer vision. The main challenges lie in capturing spatial or temporal dependencies between labels and discovering the locations of discriminative features for each class. In order to overcome these challenges, we ...