Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification
نویسندگان
چکیده
منابع مشابه
Multi-Task Label Embedding for Text Classification
Multi-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains. However, most previous works treat labels of each task as independent and meaningless onehot vectors, which cause a loss of potential information and makes it difficult for these models to jointly learn three or more tasks. In this paper, we prop...
متن کاملMulti-label Semantic Scene Classification
In classic pattern recognition problems, classes are mutually exclusive by definition. Classification errors occur when the classes overlap in the feature space. We examine a different situation, occurring when the classes are, by definition, not mutually exclusive. Such problems arise in semantic scene and document classification and in medical diagnosis. We present a framework to handle such ...
متن کاملOrder-Free RNN with Visual Attention for Multi-Label Classification
We propose a recurrent neural network (RNN) based model for image multi-label classification. Our model uniquely integrates and learning of visual attention and Long Short Term Memory (LSTM) layers, which jointly learns the labels of interest and their co-occurrences, while the associated image regions are visually attended. Different from existing approaches utilize either model in their netwo...
متن کاملMulti-Label Image Classification with Regional Latent Semantic Dependencies
Deep convolution neural networks (CNN) have demonstrated advanced performance on single-label image classification, and various progress also have been made to apply CNN methods on multi-label image classification, which requires to annotate objects, attributes, scene categories etc. in a single shot. Recent state-of-the-art approaches to multi-label image classification exploit the label depen...
متن کاملLarge Scale Multi-label Text Classification with Semantic Word Vectors
Multi-label text classification has been applied to a multitude of tasks, including document indexing, tag suggestion, and sentiment classification. However, many of these methods disregard word order, opting to use bag-of-words models or TFIDF weighting to create document vectors. With the advent of powerful semantic embeddings, such as word2vec and GloVe, we explore how word embeddings and wo...
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: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2020
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v34i07.6964