One-shot Recognition Research Articles

SMFNet: One-Shot Recognition of Chinese Character Font Based on Siamese Metric Model

SMFNet: One-Shot Recognition of Chinese Character Font Based on Siamese Metric Model

Scalable massively parallel computing using continuous-time data representation in nanoscale crossbar array.

The growth of connected intelligent devices in the Internet of Things has created a pressing need for real-time processing and understanding of large volumes of analogue data. The difficulty in boosting the computing speed renders digital computing unable to meet the demand for processing analogue information that is intrinsically continuous in magnitude and time. By utilizing a continuous data representation in a nanoscale crossbar array, parallel computing can be implemented for the direct processing of analogue information in real time. Here, we propose a scalable massively parallel computing scheme by exploiting a continuous-time data representation and frequency multiplexing in a nanoscale crossbar array. This computing scheme enables the parallel reading of stored data and the one-shot operation of matrix-matrix multiplications in the crossbar array. Furthermore, we achieve the one-shot recognition of 16 letter images based on two physically interconnected crossbar arrays and demonstrate that the processing and modulation of analogue information can be simultaneously performed in a memristive crossbar array.

WiONE: One-Shot Learning for Environment-Robust Device-Free User Authentication via Commodity Wi-Fi in Man–Machine System

User authentication is the first and most critical step in protecting a man-machine system from a malicious spoofer. However, security and privacy are just like the two sides of one coin, hard to see both at the same time, especially by the current mainstream credential- and biometric-based approaches. To this end, we propose WiONE, a safe and privacy-preserving user authentication system leveraging the ubiquitous Wi-Fi infrastructure by exploring “how you behave” rather than “who you are”. The key idea is to apply deep learning to user physical behavior captured by Wi-Fi channel state information (CSI) to identify legitimate users while rejecting spoofers. The design of WiONE faces two challenges, namely, how to capture the subtle behavior, such as a keystroke on CSI, and how to mitigate the heavy environment-specific training required by deep learning. For the former, we design a behavior enhancement model based on the Rician fading to highlight the behavior-induced information by suppressing the behavior-unrelated information on channel response. For the latter, we develop a behavior characterization method tailored for the prototypical networks to facilitate the extraction of the domain-independent behavioral features and enable one-shot recognition of a new user in a new environment. Numerous experiments are conducted in several real-world environments, and the results show that WiONE outperforms its state-of-the-art rivals in authentication performance with much less training effort.

Metric Embedding Learning on Multi-Directional Projections

Image based instance recognition is a difficult problem, in some cases even for the human eye. While latest developments in computer vision—mostly driven by deep learning—have shown that high performance models for classification or categorization can be engineered, the problem of discriminating similar objects with a low number of samples remain challenging. Advances from multi-class classification are applied for object matching problems, as the feature extraction techniques are the same; nature-inspired multi-layered convolutional nets learn the representations, and the output of such a model maps them to a multidimensional encoding space. A metric based loss brings same instance embeddings close to each other. While these solutions achieve high classification performance, low efficiency is caused by memory cost of high parameter number, which is in a relationship with input image size. Upon shrinking the input, the model requires less trainable parameters, while performance decreases. This drawback is tackled by using compressed feature extraction, e.g., projections. In this paper, a multi-directional image projection transformation with fixed vector lengths (MDIPFL) is applied for one-shot recognition tasks, trained on Siamese and Triplet architectures. Results show, that MDIPFL based approach achieves decent performance, despite of the significantly lower number of parameters.

One-Shot Recognition of Manufacturing Defects in Steel Surfaces

Quality control is an essential process in manufacturing to make the product defect-free as well as to meet customer needs. The automation of this process is important to maintain high quality along with the high manufacturing throughput. With recent developments in deep learning and computer vision technologies, it has become possible to detect various features from the images with near-human accuracy. However, many of these approaches are data intensive. Training and deployment of such a system on manufacturing floors may become expensive and time-consuming. The need for large amounts of training data is one of the limitations of the applicability of these approaches in real-world manufacturing systems. In this work, we propose the application of a Siamese convolutional neural network to do one-shot recognition for such a task. Our results demonstrate how one-shot learning can be used in quality control of steel by identification of defects on the steel surface. This method can significantly reduce the requirements of training data and can also be run in real-time.

A Human-Centered Approach to One-Shot Gesture Learning

This paper discusses the problem of one-shot gesture recognition using a human-centered approach and its potential application to fields such as human-robot interaction where the user’s intentions are indicated through spontaneous gesturing (one-shot). Casual users have limited time to learn the gestures interface, which makes one-shot recognition an attractive alternative to interface customization. In the aim of natural interaction with machines, a framework must be developed to include the ability of humans to understand gestures from a single observation. Previous approaches to one-shot gesture recognition have relied heavily on statistical and data-mining-based solutions, and have ignored the mechanisms that are used by humans to perceive and execute gestures and that can provide valuable context information. This omission has led to suboptimal solutions. The focus of this work is on the process that leads to the realization of a gesture, rather than on the gesture itself. In this case, context involves the way in which humans produce gestures—the kinematic and anthropometric characteristics. In the method presented here, the strategy is to generate a data set of realistic samples based on features extracted from a single gesture sample. These features, called the “gist of a gesture,” are considered to represent what humans remember when seeing a gesture and, later, the cognitive process involved when trying to replicate it. By adding meaningful variability to these features, a large training data set is created while preserving the fundamental structure of the original gesture. The availability of a large data set of realistic samples allows the use of training classifiers for future recognition. The performance of the method is evaluated using different lexicons, and its efficiency is compared with that of traditional N-shot learning approaches. The strength of the approach is further illustrated through human and machine recognition of gestures performed by a dual arm-robotic platform.

An Unsupervised Hierarchical Feature Learning Framework for One-Shot Image Recognition

One-shot recognition has attracted increasing attention recently, inspired by the fact that human cognitive systems could perform recognition tasks well provided only one or a few labeled training samples, in contrast to the conventional object recognition systems that require a large number of labeled training images. One-shot recognition is a visual classification task, where only one training sample is available for each object category in the target test domain, with the help of prior-knowledge data from the source domain. In this paper, we tackle this challenging one-shot recognition problem under a more exciting setting by using only unlabeled images as prior knowledge, which requires less labeling effort than previous works which adopt fully labeled data and/or a sophisticated attribute table designed by human experts. We propose a novel unsupervised hierarchical feature learning framework to learn a feature pyramid from the prior-knowledge domain. The proposed feature learning method also could be applied across multiple feature spaces. Furthermore, we propose using pyramid-matching kernels to combine multilevel features. Examining the “Animals with Attributes” and Caltech-4 data sets in our one-shot recognition setting, we show that the proposed unsupervised feature learning approach with very limited information could achieve comparable performance to that of supervised ones.