455 research outputs found
Deep Convolutional Ranking for Multilabel Image Annotation
Multilabel image annotation is one of the most important challenges in
computer vision with many real-world applications. While existing work usually
use conventional visual features for multilabel annotation, features based on
Deep Neural Networks have shown potential to significantly boost performance.
In this work, we propose to leverage the advantage of such features and analyze
key components that lead to better performances. Specifically, we show that a
significant performance gain could be obtained by combining convolutional
architectures with approximate top- ranking objectives, as thye naturally
fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset
outperforms the conventional visual features by about 10%, obtaining the best
reported performance in the literature
Love Thy Neighbors: Image Annotation by Exploiting Image Metadata
Some images that are difficult to recognize on their own may become more
clear in the context of a neighborhood of related images with similar
social-network metadata. We build on this intuition to improve multilabel image
annotation. Our model uses image metadata nonparametrically to generate
neighborhoods of related images using Jaccard similarities, then uses a deep
neural network to blend visual information from the image and its neighbors.
Prior work typically models image metadata parametrically, in contrast, our
nonparametric treatment allows our model to perform well even when the
vocabulary of metadata changes between training and testing. We perform
comprehensive experiments on the NUS-WIDE dataset, where we show that our model
outperforms state-of-the-art methods for multilabel image annotation even when
our model is forced to generalize to new types of metadata.Comment: Accepted to ICCV 201
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