12,869 research outputs found
Modeling Human Visual Search Performance on Realistic Webpages Using Analytical and Deep Learning Methods
Modeling visual search not only offers an opportunity to predict the
usability of an interface before actually testing it on real users, but also
advances scientific understanding about human behavior. In this work, we first
conduct a set of analyses on a large-scale dataset of visual search tasks on
realistic webpages. We then present a deep neural network that learns to
predict the scannability of webpage content, i.e., how easy it is for a user to
find a specific target. Our model leverages both heuristic-based features such
as target size and unstructured features such as raw image pixels. This
approach allows us to model complex interactions that might be involved in a
realistic visual search task, which can not be easily achieved by traditional
analytical models. We analyze the model behavior to offer our insights into how
the salience map learned by the model aligns with human intuition and how the
learned semantic representation of each target type relates to its visual
search performance.Comment: the 2020 CHI Conference on Human Factors in Computing System
Deep Object-Centric Representations for Generalizable Robot Learning
Robotic manipulation in complex open-world scenarios requires both reliable
physical manipulation skills and effective and generalizable perception. In
this paper, we propose a method where general purpose pretrained visual models
serve as an object-centric prior for the perception system of a learned policy.
We devise an object-level attentional mechanism that can be used to determine
relevant objects from a few trajectories or demonstrations, and then
immediately incorporate those objects into a learned policy. A task-independent
meta-attention locates possible objects in the scene, and a task-specific
attention identifies which objects are predictive of the trajectories. The
scope of the task-specific attention is easily adjusted by showing
demonstrations with distractor objects or with diverse relevant objects. Our
results indicate that this approach exhibits good generalization across object
instances using very few samples, and can be used to learn a variety of
manipulation tasks using reinforcement learning
An Adaptive Locally Connected Neuron Model: Focusing Neuron
This paper presents a new artificial neuron model capable of learning its
receptive field in the topological domain of inputs. The model provides
adaptive and differentiable local connectivity (plasticity) applicable to any
domain. It requires no other tool than the backpropagation algorithm to learn
its parameters which control the receptive field locations and apertures. This
research explores whether this ability makes the neuron focus on informative
inputs and yields any advantage over fully connected neurons. The experiments
include tests of focusing neuron networks of one or two hidden layers on
synthetic and well-known image recognition data sets. The results demonstrated
that the focusing neurons can move their receptive fields towards more
informative inputs. In the simple two-hidden layer networks, the focusing
layers outperformed the dense layers in the classification of the 2D spatial
data sets. Moreover, the focusing networks performed better than the dense
networks even when 70 of the weights were pruned. The tests on
convolutional networks revealed that using focusing layers instead of dense
layers for the classification of convolutional features may work better in some
data sets.Comment: 45 pages, a national patent filed, submitted to Turkish Patent
Office, No: -2017/17601, Date: 09.11.201
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