5,913 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
Trajectory recognition as the basis for object individuation: A functional model of object file instantiation and object token encoding
The perception of persisting visual objects is mediated by transient intermediate representations, object files, that are instantiated in response to some, but not all, visual trajectories. The standard object file concept does not, however, provide a mechanism sufficient to account for all experimental data on visual object persistence, object tracking, and the ability to perceive spatially-disconnected stimuli as coherent objects. Based on relevant anatomical, functional, and developmental data, a functional model is developed that bases object individuation on the specific recognition of visual trajectories. This model is shown to account for a wide range of data, and to generate a variety of testable predictions. Individual variations of the model parameters are expected to generate distinct trajectory and object recognition abilities. Over-encoding of trajectory information in stored object tokens in early infancy, in particular, is expected to disrupt the ability to re-identify individuals across perceptual episodes, and lead to developmental outcomes with characteristics of autism spectrum disorders
Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work
Inspired by the fact that human brains can emphasize discriminative parts of
the input and suppress irrelevant ones, substantial local mechanisms have been
designed to boost the development of computer vision. They can not only focus
on target parts to learn discriminative local representations, but also process
information selectively to improve the efficiency. In terms of application
scenarios and paradigms, local mechanisms have different characteristics. In
this survey, we provide a systematic review of local mechanisms for various
computer vision tasks and approaches, including fine-grained visual
recognition, person re-identification, few-/zero-shot learning, multi-modal
learning, self-supervised learning, Vision Transformers, and so on.
Categorization of local mechanisms in each field is summarized. Then,
advantages and disadvantages for every category are analyzed deeply, leaving
room for exploration. Finally, future research directions about local
mechanisms have also been discussed that may benefit future works. To the best
our knowledge, this is the first survey about local mechanisms on computer
vision. We hope that this survey can shed light on future research in the
computer vision field
- …