Attribute relation learning for zero-shot classification

Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and undesirable in practical tasks. This paper aims to explore how to accurately classify new hyperspectral images with only a few labeled samples, i.e., the hyperspectral images few-shot classification. Specifically, we design a new deep classification model based on relational network and train it with the idea of meta-learning. Firstly, the feature learning module and the relation learning module of the model can make full use of the spatial–spectral information in hyperspectral images and carry out relation learning by comparing the similarity between samples. Secondly, the task-based learning strategy can enable the model to continuously enhance its ability to learn how to learn with a large number of tasks randomly generated from different data sets. Benefitting from the above two points, the proposed method has excellent generalization ability and can obtain satisfactory classification results with only a few labeled samples. In order to verify the performance of the proposed method, experiments were carried out on three public data sets. The results indicate that the proposed method can achieve better classification results than the traditional semisupervised support vector machine and semisupervised deep learning models.

Download Full-text

Actual issues of systematization of scientific knowledge on the conditions of shot in the forensic ballistics

Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia ◽

10.35750/2071-8284-2020-3-164-169 ◽

2020 ◽

Vol 2020 (3) ◽

pp. 164-169

Author(s):

Igor' Latyshov ◽

Fedor Samuylenko

Keyword(s):

Scientific Knowledge ◽

Communication Systems ◽

Production Method ◽

Natural Phenomenon ◽

Projectile Energy ◽

Energy Characteristics ◽

Shot Classification ◽

Forensic Ballistics

In this research, there was considered a challenge of constructing a system of scientific knowledge of the shot conditions in judicial ballistics. It was observed that there are underlying factors that are intended to ensureits [scientific knowledge] consistency: identification of the list of shot conditions, which require consideration when solving expert-level research tasks on weapons, cartridges and traces of their action; determination of the communication systems in the course of objects’ interaction, which present the result of exposure to the conditions of the shot; classification of the shot conditions based on the grounds significant for solving scientific and practical problems. The article contains the characteristics of a constructive, functional factor (condition) of weapons and cartridges influence, environmental and fire factors, the structure of the target and its physical properties, situational and spatial factors, and projectile energy characteristics. Highlighted are the forms of connections formed in the course of objects’ interaction, proposed are the author’s classifications of forensically significant shooting conditions with them being divided on the basis of the following criteria: production from the object of interaction, production from a natural phenomenon, production method, results weapon operation and utilization, duration of exposure, type of structural connections between interaction objects, number of conditions that apply when firing and the forming traces.

Download Full-text

Robust Re-weighting Prototypical Networks for Few-Shot Classification

2020 6th International Conference on Robotics and Artificial Intelligence ◽

10.1145/3449301.3449325 ◽

2020 ◽

Author(s):

Junjie Zhu ◽

Xiaodong Yi ◽

Naiyang Guan ◽

Hang Cheng

Keyword(s):

Shot Classification

Download Full-text

Meta Learning for Few-Shot One-Class Classification

AI ◽

10.3390/ai2020012 ◽

2021 ◽

Vol 2 (2) ◽

pp. 195-208

Author(s):

Gabriel Dahia ◽

Maurício Pamplona Segundo

Keyword(s):

Feature Representation ◽

Support Vector ◽

Support Vector Data Description ◽

Target Class ◽

Feature Representations ◽

Shot Classification ◽

Training Stage ◽

Comparable Performance ◽

Meta Learning ◽

One Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

Download Full-text