35,465 research outputs found
A Deep Multi-Level Attentive network for Multimodal Sentiment Analysis
Multimodal sentiment analysis has attracted increasing attention with broad
application prospects. The existing methods focuses on single modality, which
fails to capture the social media content for multiple modalities. Moreover, in
multi-modal learning, most of the works have focused on simply combining the
two modalities, without exploring the complicated correlations between them.
This resulted in dissatisfying performance for multimodal sentiment
classification. Motivated by the status quo, we propose a Deep Multi-Level
Attentive network, which exploits the correlation between image and text
modalities to improve multimodal learning. Specifically, we generate the
bi-attentive visual map along the spatial and channel dimensions to magnify
CNNs representation power. Then we model the correlation between the image
regions and semantics of the word by extracting the textual features related to
the bi-attentive visual features by applying semantic attention. Finally,
self-attention is employed to automatically fetch the sentiment-rich multimodal
features for the classification. We conduct extensive evaluations on four
real-world datasets, namely, MVSA-Single, MVSA-Multiple, Flickr, and Getty
Images, which verifies the superiority of our method.Comment: 11 pages, 7 figure
Multimodal Classification of Urban Micro-Events
In this paper we seek methods to effectively detect urban micro-events. Urban
micro-events are events which occur in cities, have limited geographical
coverage and typically affect only a small group of citizens. Because of their
scale these are difficult to identify in most data sources. However, by using
citizen sensing to gather data, detecting them becomes feasible. The data
gathered by citizen sensing is often multimodal and, as a consequence, the
information required to detect urban micro-events is distributed over multiple
modalities. This makes it essential to have a classifier capable of combining
them. In this paper we explore several methods of creating such a classifier,
including early, late, hybrid fusion and representation learning using
multimodal graphs. We evaluate performance on a real world dataset obtained
from a live citizen reporting system. We show that a multimodal approach yields
higher performance than unimodal alternatives. Furthermore, we demonstrate that
our hybrid combination of early and late fusion with multimodal embeddings
performs best in classification of urban micro-events
Learning Social Image Embedding with Deep Multimodal Attention Networks
Learning social media data embedding by deep models has attracted extensive
research interest as well as boomed a lot of applications, such as link
prediction, classification, and cross-modal search. However, for social images
which contain both link information and multimodal contents (e.g., text
description, and visual content), simply employing the embedding learnt from
network structure or data content results in sub-optimal social image
representation. In this paper, we propose a novel social image embedding
approach called Deep Multimodal Attention Networks (DMAN), which employs a deep
model to jointly embed multimodal contents and link information. Specifically,
to effectively capture the correlations between multimodal contents, we propose
a multimodal attention network to encode the fine-granularity relation between
image regions and textual words. To leverage the network structure for
embedding learning, a novel Siamese-Triplet neural network is proposed to model
the links among images. With the joint deep model, the learnt embedding can
capture both the multimodal contents and the nonlinear network information.
Extensive experiments are conducted to investigate the effectiveness of our
approach in the applications of multi-label classification and cross-modal
search. Compared to state-of-the-art image embeddings, our proposed DMAN
achieves significant improvement in the tasks of multi-label classification and
cross-modal search
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