165,812 research outputs found
Teaching Robots Novel Objects by Pointing at Them
Robots that must operate in novel environments and collaborate with humans
must be capable of acquiring new knowledge from human experts during operation.
We propose teaching a robot novel objects it has not encountered before by
pointing a hand at the new object of interest. An end-to-end neural network is
used to attend to the novel object of interest indicated by the pointing hand
and then to localize the object in new scenes. In order to attend to the novel
object indicated by the pointing hand, we propose a spatial attention
modulation mechanism that learns to focus on the highlighted object while
ignoring the other objects in the scene. We show that a robot arm can
manipulate novel objects that are highlighted by pointing a hand at them. We
also evaluate the performance of the proposed architecture on a synthetic
dataset constructed using emojis and on a real-world dataset of common objects
A Role for the prefrontal cortex in supporting singular demonstrative reference
One of the most pressing questions concerning singular demonstrative mental contents is what makes their content singular: that is to say, what makes it the case that individual objects are the representata of these mental states. Many philosophers have required sophisticated intellectual capacities for singular content to be possible, such as the possession of an elaborate scheme of space and time. A more recent reaction to this strategy proposes to account for singular content solely on the basis of empirical models of visual processing. We believe both sides make good points, and offer an intermediate way of looking into singular content. Our suggestion is that singular content may be traced to psychological capacities to form flexible, abstract representations in the prefrontal cortex. This allows them to be sustained for increasingly longer periods of time and extrapolated beyond the context of perception, thus going beyond lowlevel sensory representations while also falling short of more sophisticated intellectual abilities
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
TThe goal of our work is to discover dominant objects in a very general
setting where only a single unlabeled image is given. This is far more
challenge than typical co-localization or weakly-supervised localization tasks.
To tackle this problem, we propose a simple but effective pattern mining-based
method, called Object Location Mining (OLM), which exploits the advantages of
data mining and feature representation of pre-trained convolutional neural
networks (CNNs). Specifically, we first convert the feature maps from a
pre-trained CNN model into a set of transactions, and then discovers frequent
patterns from transaction database through pattern mining techniques. We
observe that those discovered patterns, i.e., co-occurrence highlighted
regions, typically hold appearance and spatial consistency. Motivated by this
observation, we can easily discover and localize possible objects by merging
relevant meaningful patterns. Extensive experiments on a variety of benchmarks
demonstrate that OLM achieves competitive localization performance compared
with the state-of-the-art methods. We also evaluate our approach compared with
unsupervised saliency detection methods and achieves competitive results on
seven benchmark datasets. Moreover, we conduct experiments on fine-grained
classification to show that our proposed method can locate the entire object
and parts accurately, which can benefit to improving the classification results
significantly
Swarm-Based Spatial Sorting
Purpose: To present an algorithm for spatially sorting objects into an
annular structure. Design/Methodology/Approach: A swarm-based model that
requires only stochastic agent behaviour coupled with a pheromone-inspired
"attraction-repulsion" mechanism. Findings: The algorithm consistently
generates high-quality annular structures, and is particularly powerful in
situations where the initial configuration of objects is similar to those
observed in nature. Research limitations/implications: Experimental evidence
supports previous theoretical arguments about the nature and mechanism of
spatial sorting by insects. Practical implications: The algorithm may find
applications in distributed robotics. Originality/value: The model offers a
powerful minimal algorithmic framework, and also sheds further light on the
nature of attraction-repulsion algorithms and underlying natural processes.Comment: Accepted by the Int. J. Intelligent Computing and Cybernetic
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