46,946 research outputs found
Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence
This paper applies state-of-the-art techniques in deep learning and computer
vision to measure visual similarities between architectural designs by
different architects. Using a dataset consisting of web scraped images and an
original collection of images of architectural works, we first train a deep
convolutional neural network (DCNN) model capable of achieving 73% accuracy in
classifying works belonging to 34 different architects. Through examining the
weights in the trained DCNN model, we are able to quantitatively measure the
visual similarities between architects that are implicitly learned by our
model. Using this measure, we cluster architects that are identified to be
similar and compare our findings to conventional classification made by
architectural historians and theorists. Our clustering of architectural designs
remarkably corroborates conventional views in architectural history, and the
learned architectural features also coheres with the traditional understanding
of architectural designs.Comment: 22 pages, 5 figures, 4 table
Complex Event Recognition from Images with Few Training Examples
We propose to leverage concept-level representations for complex event
recognition in photographs given limited training examples. We introduce a
novel framework to discover event concept attributes from the web and use that
to extract semantic features from images and classify them into social event
categories with few training examples. Discovered concepts include a variety of
objects, scenes, actions and event sub-types, leading to a discriminative and
compact representation for event images. Web images are obtained for each
discovered event concept and we use (pretrained) CNN features to train concept
classifiers. Extensive experiments on challenging event datasets demonstrate
that our proposed method outperforms several baselines using deep CNN features
directly in classifying images into events with limited training examples. We
also demonstrate that our method achieves the best overall accuracy on a
dataset with unseen event categories using a single training example.Comment: Accepted to Winter Applications of Computer Vision (WACV'17
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%
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