73,470 research outputs found
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
High accuracy video label prediction (classification) models are attributed
to large scale data. These data could be frame feature sequences extracted by a
pre-trained convolutional-neural-network, which promote the efficiency for
creating models. Unsupervised solutions such as feature average pooling, as a
simple label-independent parameter-free based method, has limited ability to
represent the video. While the supervised methods, like RNN, can greatly
improve the recognition accuracy. However, the video length is usually long,
and there are hierarchical relationships between frames across events in the
video, the performance of RNN based models are decreased. In this paper, we
proposes a novel video classification method based on a deep convolutional
graph neural network(DCGN). The proposed method utilize the characteristics of
the hierarchical structure of the video, and performed multi-level feature
extraction on the video frame sequence through the graph network, obtained a
video representation re ecting the event semantics hierarchically. We test our
model on YouTube-8M Large-Scale Video Understanding dataset, and the result
outperforms RNN based benchmarks.Comment: ECCV 201
Neural Approaches to Relational Aspect-Based Sentiment Analysis. Exploring generalizations across words and languages
Jebbara S. Neural Approaches to Relational Aspect-Based Sentiment Analysis. Exploring generalizations across words and languages. Bielefeld: Universität Bielefeld; 2020.Everyday, vast amounts of unstructured, textual data are shared online in digital form.
Websites such as forums, social media sites, review sites, blogs, and comment sections offer platforms to express and discuss opinions and experiences. Understanding the opinions in these resources is valuable for e.g. businesses to support market research and customer service but also individuals, who can benefit from the experiences and expertise of others.
In this thesis, we approach the topic of opinion extraction and classification with neural network models. We regard this area of sentiment analysis as a relation extraction problem in which the sentiment of some opinion holder towards a certain aspect of a product, theme, or event needs to be extracted. In accordance with this framework, our main contributions are the following:
1. We propose a full system addressing all subtasks of relational sentiment analysis.
2. We investigate how semantic web resources can be leveraged in a neural-network-based model for the extraction of opinion targets and the classification of sentiment labels. Specifically, we experiment with enhancing pretrained word embeddings using the lexical resource WordNet. Furthermore, we enrich a purely text-based model with SenticNet concepts and observe an improvement for sentiment classification.
3. We examine how opinion targets can be automatically identified in noisy texts. Customer reviews, for instance, are prone to contain misspelled words and are difficult to process due to their domain-specific language. We integrate information about the character structure of a word into a sequence labeling system using character-level word embeddings and show their positive impact on the system's performance. We reveal encoded character patterns of the learned embeddings and give a nuanced view of the obtained performance differences.
4. Opinion target extraction usually relies on supervised learning approaches. We address the lack of available annotated data for specific languages by proposing a zero-shot cross-lingual approach for the extraction of opinion target expressions. We leverage multilingual word embeddings that share a common vector space across various languages and incorporate these into a convolutional neural network architecture. Our experiments with 5 languages give promising results: We can successfully train a model on annotated data of a source language and perform accurate prediction on a target language without ever using any annotated samples in that target language
Attention Mechanisms for Object Recognition with Event-Based Cameras
Event-based cameras are neuromorphic sensors capable of efficiently encoding
visual information in the form of sparse sequences of events. Being
biologically inspired, they are commonly used to exploit some of the
computational and power consumption benefits of biological vision. In this
paper we focus on a specific feature of vision: visual attention. We propose
two attentive models for event based vision: an algorithm that tracks events
activity within the field of view to locate regions of interest and a
fully-differentiable attention procedure based on DRAW neural model. We
highlight the strengths and weaknesses of the proposed methods on four
datasets, the Shifted N-MNIST, Shifted MNIST-DVS, CIFAR10-DVS and N-Caltech101
collections, using the Phased LSTM recognition network as a baseline reference
model obtaining improvements in terms of both translation and scale invariance.Comment: WACV2019 camera-ready submissio
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