26,494 research outputs found
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition
Indoor scene recognition is a multi-faceted and challenging problem due to
the diverse intra-class variations and the confusing inter-class similarities.
This paper presents a novel approach which exploits rich mid-level
convolutional features to categorize indoor scenes. Traditionally used
convolutional features preserve the global spatial structure, which is a
desirable property for general object recognition. However, we argue that this
structuredness is not much helpful when we have large variations in scene
layouts, e.g., in indoor scenes. We propose to transform the structured
convolutional activations to another highly discriminative feature space. The
representation in the transformed space not only incorporates the
discriminative aspects of the target dataset, but it also encodes the features
in terms of the general object categories that are present in indoor scenes. To
this end, we introduce a new large-scale dataset of 1300 object categories
which are commonly present in indoor scenes. Our proposed approach achieves a
significant performance boost over previous state of the art approaches on five
major scene classification datasets
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work
Deep networks thrive when trained on large scale data collections. This has
given ImageNet a central role in the development of deep architectures for
visual object classification. However, ImageNet was created during a specific
period in time, and as such it is prone to aging, as well as dataset bias
issues. Moving beyond fixed training datasets will lead to more robust visual
systems, especially when deployed on robots in new environments which must
train on the objects they encounter there. To make this possible, it is
important to break free from the need for manual annotators. Recent work has
begun to investigate how to use the massive amount of images available on the
Web in place of manual image annotations. We contribute to this research thread
with two findings: (1) a study correlating a given level of noisily labels to
the expected drop in accuracy, for two deep architectures, on two different
types of noise, that clearly identifies GoogLeNet as a suitable architecture
for learning from Web data; (2) a recipe for the creation of Web datasets with
minimal noise and maximum visual variability, based on a visual and natural
language processing concept expansion strategy. By combining these two results,
we obtain a method for learning powerful deep object models automatically from
the Web. We confirm the effectiveness of our approach through object
categorization experiments using our Web-derived version of ImageNet on a
popular robot vision benchmark database, and on a lifelong object discovery
task on a mobile robot.Comment: 8 pages, 7 figures, 3 table
- …