Classification-reconstruction learning for open-set recognition

Open-set classification is a problem of handling `unknown' classes that are not contained in the training dataset, whereas traditional classifiers assume that only known classes appear in the test environment. Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set; this causes specialization of learned representations to known classes and makes it hard to distinguish unknowns from knowns. In contrast, we train networks for joint classification and reconstruction of input data. This enhances the learned representation so as to preserve information useful for separating unknowns from knowns, as well as to discriminate classes of knowns. Our novel Classification-Reconstruction learning for Open-Set Recognition (CROSR) utilizes latent representations for reconstruction and enables robust unknown detection without harming the known-class classification accuracy. Extensive experiments reveal that the proposed method outperforms existing deep open-set classifiers in multiple standard datasets and is robust to diverse outliers. The code is available in https://nae-lab.org/~rei/research/crosr/.Comment: 11 pages, 7 figure

Revealing the molecular signatures of host-pathogen interactions.

Advances in sequencing technology and genome-wide association studies are now revealing the complex interactions between hosts and pathogen through genomic variation signatures, which arise from evolutionary co-existence

A vision-based system to support tactical and physical analyses in futsal

This paper presents a vision-based system to support tactical and physical analyses of futsal teams. Most part of the current analyses in this sport are manually performed, while the existing solutions based on automatic approaches are frequently composed of costly and complex tools, developed for other kind of team sports, making it difficult their adoption by futsal teams. Our system, on the other hand, represents a simple yet efficient dedicated solution, which is based on the analyses of image sequences captured by a single stationary camera used to obtain top-view images of the entire court. We use adaptive background subtraction and blob analysis to detect players, as well as particle filters to track them in every video frame. The system determines the distance traveled by each player, his/her mean and maximum speeds, as well as generates heat maps that describe players’ occupancy during the match. To present the collected data, our system uses a specially developed mobile application. Experimental results with image sequences of an official match and a training match show that our system provides data with global mean tracking errors below 40 cm, demanding on 25 ms to process each frame and, thus, demonstrating its high application potential

Shadow Generation in Mixed Reality: A Comprehensive Survey

This paper provides an overview of the issues and techniques involved in shadow generation in mixed reality environments. Shadow generation techniques in virtual environments are explained briefly. The key factors characterizing the well-known techniques are described in detail and the pros and cons of each technique are discussed. The conceptual perspective, the improvements, and future techniques are also investigated, summarized, and analysed in depth. This paper aims to provide researchers with a solid background on the state-of-the-art implementation of shadows in mixed reality. Thus, this could make it easier to choose the most appropriate method to achieve the aims. It is also hoped that this analysis will help researchers find solutions to the problems facing each technique