37,609 research outputs found
Some like it hot - visual guidance for preference prediction
For people first impressions of someone are of determining importance. They
are hard to alter through further information. This begs the question if a
computer can reach the same judgement. Earlier research has already pointed out
that age, gender, and average attractiveness can be estimated with reasonable
precision. We improve the state-of-the-art, but also predict - based on
someone's known preferences - how much that particular person is attracted to a
novel face. Our computational pipeline comprises a face detector, convolutional
neural networks for the extraction of deep features, standard support vector
regression for gender, age and facial beauty, and - as the main novelties -
visual regularized collaborative filtering to infer inter-person preferences as
well as a novel regression technique for handling visual queries without rating
history. We validate the method using a very large dataset from a dating site
as well as images from celebrities. Our experiments yield convincing results,
i.e. we predict 76% of the ratings correctly solely based on an image, and
reveal some sociologically relevant conclusions. We also validate our
collaborative filtering solution on the standard MovieLens rating dataset,
augmented with movie posters, to predict an individual's movie rating. We
demonstrate our algorithms on howhot.io which went viral around the Internet
with more than 50 million pictures evaluated in the first month.Comment: accepted for publication at CVPR 201
Digging Deeper into Egocentric Gaze Prediction
This paper digs deeper into factors that influence egocentric gaze. Instead
of training deep models for this purpose in a blind manner, we propose to
inspect factors that contribute to gaze guidance during daily tasks. Bottom-up
saliency and optical flow are assessed versus strong spatial prior baselines.
Task-specific cues such as vanishing point, manipulation point, and hand
regions are analyzed as representatives of top-down information. We also look
into the contribution of these factors by investigating a simple recurrent
neural model for ego-centric gaze prediction. First, deep features are
extracted for all input video frames. Then, a gated recurrent unit is employed
to integrate information over time and to predict the next fixation. We also
propose an integrated model that combines the recurrent model with several
top-down and bottom-up cues. Extensive experiments over multiple datasets
reveal that (1) spatial biases are strong in egocentric videos, (2) bottom-up
saliency models perform poorly in predicting gaze and underperform spatial
biases, (3) deep features perform better compared to traditional features, (4)
as opposed to hand regions, the manipulation point is a strong influential cue
for gaze prediction, (5) combining the proposed recurrent model with bottom-up
cues, vanishing points and, in particular, manipulation point results in the
best gaze prediction accuracy over egocentric videos, (6) the knowledge
transfer works best for cases where the tasks or sequences are similar, and (7)
task and activity recognition can benefit from gaze prediction. Our findings
suggest that (1) there should be more emphasis on hand-object interaction and
(2) the egocentric vision community should consider larger datasets including
diverse stimuli and more subjects.Comment: presented at WACV 201
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering methods. Another thread is to transfer knowledge from other
source domains such as improving the movie recommendation with the knowledge
from the book domain, leading to transfer learning methods. In real-world life,
no single service can satisfy a user's all information needs. Thus it motivates
us to exploit both auxiliary and source information for RSs in this paper. We
propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH)
methods for cross-domain recommendation with unstructured text in an end-to-end
manner. TMH attentively extracts useful content from unstructured text via a
memory module and selectively transfers knowledge from a source domain via a
transfer network. On two real-world datasets, TMH shows better performance in
terms of three ranking metrics by comparing with various baselines. We conduct
thorough analyses to understand how the text content and transferred knowledge
help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape
Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition
Recently, there has been a lot of interest in automatically generating
descriptions for an image. Most existing language-model based approaches for
this task learn to generate an image description word by word in its original
word order. However, for humans, it is more natural to locate the objects and
their relationships first, and then elaborate on each object, describing
notable attributes. We present a coarse-to-fine method that decomposes the
original image description into a skeleton sentence and its attributes, and
generates the skeleton sentence and attribute phrases separately. By this
decomposition, our method can generate more accurate and novel descriptions
than the previous state-of-the-art. Experimental results on the MS-COCO and a
larger scale Stock3M datasets show that our algorithm yields consistent
improvements across different evaluation metrics, especially on the SPICE
metric, which has much higher correlation with human ratings than the
conventional metrics. Furthermore, our algorithm can generate descriptions with
varied length, benefiting from the separate control of the skeleton and
attributes. This enables image description generation that better accommodates
user preferences.Comment: Accepted by CVPR 201
Optimal learning spaces: design implications for primary schools
Review guide of the design evidence for primary school
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