977 research outputs found
Multimodal sequential fashion attribute prediction
We address multimodal product attribute prediction of fashion items based on product images and titles. The product attributes, such as type, sub-type, cut or fit, are in a chain format, with previous attribute values constraining the values of the next attributes. We propose to address this task with a sequential prediction model that can learn to capture the dependencies between the different attribute values in the chain. Our experiments on three product datasets show that the sequential model outperforms two non-sequential baselines on all experimental datasets. Compared to other models, the sequential model is also better able to generate sequences of attribute chains not seen during training. We also measure the contributions of both image and textual input and show that while text-only models always outperform image-only models, only the multimodal sequential model combining both image and text improves over the text-only model on all experimental dataset
Facial Expression Recognition from World Wild Web
Recognizing facial expression in a wild setting has remained a challenging
task in computer vision. The World Wide Web is a good source of facial images
which most of them are captured in uncontrolled conditions. In fact, the
Internet is a Word Wild Web of facial images with expressions. This paper
presents the results of a new study on collecting, annotating, and analyzing
wild facial expressions from the web. Three search engines were queried using
1250 emotion related keywords in six different languages and the retrieved
images were mapped by two annotators to six basic expressions and neutral. Deep
neural networks and noise modeling were used in three different training
scenarios to find how accurately facial expressions can be recognized when
trained on noisy images collected from the web using query terms (e.g. happy
face, laughing man, etc)? The results of our experiments show that deep neural
networks can recognize wild facial expressions with an accuracy of 82.12%
Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging
Molecular classification has transformed the management of brain tumors by
enabling more accurate prognostication and personalized treatment. However,
timely molecular diagnostic testing for patients with brain tumors is limited,
complicating surgical and adjuvant treatment and obstructing clinical trial
enrollment. In this study, we developed DeepGlioma, a rapid ( seconds),
artificial-intelligence-based diagnostic screening system to streamline the
molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a
multimodal dataset that includes stimulated Raman histology (SRH); a rapid,
label-free, non-consumptive, optical imaging method; and large-scale, public
genomic data. In a prospective, multicenter, international testing cohort of
patients with diffuse glioma () who underwent real-time SRH imaging, we
demonstrate that DeepGlioma can predict the molecular alterations used by the
World Health Organization to define the adult-type diffuse glioma taxonomy (IDH
mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular
classification accuracy of . Our results represent how
artificial intelligence and optical histology can be used to provide a rapid
and scalable adjunct to wet lab methods for the molecular screening of patients
with diffuse glioma.Comment: Paper published in Nature Medicin
Support matrix machine: A review
Support vector machine (SVM) is one of the most studied paradigms in the
realm of machine learning for classification and regression problems. It relies
on vectorized input data. However, a significant portion of the real-world data
exists in matrix format, which is given as input to SVM by reshaping the
matrices into vectors. The process of reshaping disrupts the spatial
correlations inherent in the matrix data. Also, converting matrices into
vectors results in input data with a high dimensionality, which introduces
significant computational complexity. To overcome these issues in classifying
matrix input data, support matrix machine (SMM) is proposed. It represents one
of the emerging methodologies tailored for handling matrix input data. The SMM
method preserves the structural information of the matrix data by using the
spectral elastic net property which is a combination of the nuclear norm and
Frobenius norm. This article provides the first in-depth analysis of the
development of the SMM model, which can be used as a thorough summary by both
novices and experts. We discuss numerous SMM variants, such as robust, sparse,
class imbalance, and multi-class classification models. We also analyze the
applications of the SMM model and conclude the article by outlining potential
future research avenues and possibilities that may motivate academics to
advance the SMM algorithm
Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes
In this paper, we present a label transfer model from texts to images for
image classification tasks. The problem of image classification is often much
more challenging than text classification. On one hand, labeled text data is
more widely available than the labeled images for classification tasks. On the
other hand, text data tends to have natural semantic interpretability, and they
are often more directly related to class labels. On the contrary, the image
features are not directly related to concepts inherent in class labels. One of
our goals in this paper is to develop a model for revealing the functional
relationships between text and image features as to directly transfer
intermodal and intramodal labels to annotate the images. This is implemented by
learning a transfer function as a bridge to propagate the labels between two
multimodal spaces. However, the intermodal label transfers could be undermined
by blindly transferring the labels of noisy texts to annotate images. To
mitigate this problem, we present an intramodal label transfer process, which
complements the intermodal label transfer by transferring the image labels
instead when relevant text is absent from the source corpus. In addition, we
generalize the inter-modal label transfer to zero-shot learning scenario where
there are only text examples available to label unseen classes of images
without any positive image examples. We evaluate our algorithm on an image
classification task and show the effectiveness with respect to the other
compared algorithms.Comment: The paper has been accepted by IEEE Transactions on Pattern Analysis
and Machine Intelligence. It will apear in a future issu
Learning recurrent representations for hierarchical behavior modeling
We propose a framework for detecting action patterns from motion sequences
and modeling the sensory-motor relationship of animals, using a generative
recurrent neural network. The network has a discriminative part (classifying
actions) and a generative part (predicting motion), whose recurrent cells are
laterally connected, allowing higher levels of the network to represent high
level phenomena. We test our framework on two types of data, fruit fly behavior
and online handwriting. Our results show that 1) taking advantage of unlabeled
sequences, by predicting future motion, significantly improves action detection
performance when training labels are scarce, 2) the network learns to represent
high level phenomena such as writer identity and fly gender, without
supervision, and 3) simulated motion trajectories, generated by treating motion
prediction as input to the network, look realistic and may be used to
qualitatively evaluate whether the model has learnt generative control rules
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