Learning Cross-Modal Aligned Representation With Graph Embedding
نویسندگان
چکیده
منابع مشابه
Cross-view Graph Embedding
Recently, more and more approaches are emerging to solve the cross-view matching problem where reference samples and query samples are from different views. In this paper, inspired by Graph Embedding, we propose a unified framework for these cross-view methods called Cross-view Graph Embedding. The proposed framework can not only reformulate most traditional cross-view methods (e.g., CCA, PLS a...
متن کاملCross-modal Common Representation Learning by Hybrid Transfer Network
DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities as image and text, but existing methods often face the challenge of insufficient cross-modal training data. In single-modal scenario, similar problem is usually relieved by transferring knowledge from largescale auxiliary datasets (as ImageNet). Knowledge from such single-modal datasets is also very us...
متن کاملLearning Graph Representations with Embedding Propagation
Label Representations • Let l ∈ Rd be the representation of label l, and f be a differentiable embedding function • For labels of label type i, we apply a learnable embedding function l = fi(l) • hi(v) is the embedding of label type i for vertex v: hi(v) = gi ({l | l ∈ labels of type i associated with vertex v}) • h̃i(v) is the reconstruction of the embedding of label type i for vertex v: h̃i(v) ...
متن کاملActive Learning for Graph Embedding
Graph embedding provides an ecient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embeddings can be processed eciently in terms of both time and space. Current semi-supervised graph embedding algorithms assume the labelled nodes are given, which may not be alwa...
متن کاملCombining graph embedding and sparse regression with structure low-rank representation for semi-supervised learning
Introduction Complex adaptive systems (CAS) research area is trying to establish a comprehensive and general understanding of the complex world around us (Niazi and Hussain 2013). Complex systems typically involve the generation of high dimensional data and rely on effective analysis and management of such high-dimensional data. High dimensional data exists in a wide variety of real application...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2018
ISSN: 2169-3536
DOI: 10.1109/access.2018.2881997