146 research outputs found
Pseudo-labels for Supervised Learning on Dynamic Vision Sensor Data, Applied to Object Detection under Ego-motion
In recent years, dynamic vision sensors (DVS), also known as event-based
cameras or neuromorphic sensors, have seen increased use due to various
advantages over conventional frame-based cameras. Using principles inspired by
the retina, its high temporal resolution overcomes motion blurring, its high
dynamic range overcomes extreme illumination conditions and its low power
consumption makes it ideal for embedded systems on platforms such as drones and
self-driving cars. However, event-based data sets are scarce and labels are
even rarer for tasks such as object detection. We transferred discriminative
knowledge from a state-of-the-art frame-based convolutional neural network
(CNN) to the event-based modality via intermediate pseudo-labels, which are
used as targets for supervised learning. We show, for the first time,
event-based car detection under ego-motion in a real environment at 100 frames
per second with a test average precision of 40.3% relative to our annotated
ground truth. The event-based car detector handles motion blur and poor
illumination conditions despite not explicitly trained to do so, and even
complements frame-based CNN detectors, suggesting that it has learnt
generalized visual representations
Intelligent Computing: The Latest Advances, Challenges and Future
Computing is a critical driving force in the development of human
civilization. In recent years, we have witnessed the emergence of intelligent
computing, a new computing paradigm that is reshaping traditional computing and
promoting digital revolution in the era of big data, artificial intelligence
and internet-of-things with new computing theories, architectures, methods,
systems, and applications. Intelligent computing has greatly broadened the
scope of computing, extending it from traditional computing on data to
increasingly diverse computing paradigms such as perceptual intelligence,
cognitive intelligence, autonomous intelligence, and human-computer fusion
intelligence. Intelligence and computing have undergone paths of different
evolution and development for a long time but have become increasingly
intertwined in recent years: intelligent computing is not only
intelligence-oriented but also intelligence-driven. Such cross-fertilization
has prompted the emergence and rapid advancement of intelligent computing.
Intelligent computing is still in its infancy and an abundance of innovations
in the theories, systems, and applications of intelligent computing are
expected to occur soon. We present the first comprehensive survey of literature
on intelligent computing, covering its theory fundamentals, the technological
fusion of intelligence and computing, important applications, challenges, and
future perspectives. We believe that this survey is highly timely and will
provide a comprehensive reference and cast valuable insights into intelligent
computing for academic and industrial researchers and practitioners
Research on Brain and Mind Inspired Intelligence
To address the problems of scientific theory, common technology and engineering application of multimedia and multimodal information computing, this paper is focused on the theoretical model, algorithm framework, and system architecture of brain and mind inspired intelligence (BMI) based on the structure mechanism simulation of the nervous system, the function architecture emulation of the cognitive system and the complex behavior imitation of the natural system. Based on information theory, system theory, cybernetics and bionics, we define related concept and hypothesis of brain and mind inspired computing (BMC) and design a model and framework for frontier BMI theory. Research shows that BMC can effectively improve the performance of semantic processing of multimedia and cross-modal information, such as target detection, classification and recognition. Based on the brain mechanism and mind architecture, a semantic-oriented multimedia neural, cognitive computing model is designed for multimedia semantic computing. Then a hierarchical cross-modal cognitive neural computing framework is proposed for cross-modal information processing. Furthermore, a cross-modal neural, cognitive computing architecture is presented for remote sensing intelligent information extraction platform and unmanned autonomous system
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Intelligent computing : the latest advances, challenges and future
Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing
- …