다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

비만아 부모의 돌봄 경험: q 방법론

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

Deep face recognition: A survey

A survey on deep learning based face recognition

Temporal Point Processes (TPPs) are often used to represent the sequence of events ordered as per the time of occurrence. Owing to their flexible nature, TPPs have been used to model different scenarios and have shown applicability in various real-world applications. While TPPs focus on modeling the event occurrence, Marked Temporal Point Process (MTPP) focuses on modeling the category/class of the event as well (termed as the marker). Research in MTPP has garnered substantial attention over the past few years, with an extensive focus on supervised algorithms. Despite the research focus, limited attention has been given to the challenging problem of developing solutions in semi-supervised settings, where algorithms have access to a mix of labeled and unlabeled data. This research proposes a novel algorithm for Semi-supervised Learning for Marked Temporal Point Processes (SSL-MTPP) applicable in such scenarios. The proposed SSL-MTPP algorithm utilizes a combination of labeled and unlabeled data for learning a robust marker prediction model. The proposed algorithm utilizes an RNN-based Encoder-Decoder module for learning effective representations of the time sequence. The efficacy of the proposed algorithm has been demonstrated via multiple protocols on the Retweet dataset, where the proposed SSL-MTPP demonstrates improved performance in comparison to the traditional supervised learning approach.

Semi-supervised Learning for Marked Temporal Point Processes.

Editorial Introduction to the Special Issue on Biometrics at a Distance in the Deep Learning Era

Automated document analysis and parsing has been the focus of research since a long time. An important component of document parsing revolves around understanding tabular regions with respect to their structure identification, followed by precise information extraction. While substantial effort has gone into table detection and information extraction from documents, table structure recognition remains to be a long-standing task demanding dedicated attention. The identification of the table structure enables extraction of structured information from tabular regions which can then be utilized for further applications. To this effect, this research proposes a novel table structure recognition pipeline consisting of row identification and column identification modules. The column identification module utilizes a novel Column Detector Encoder-Decoder model (termed as CoDec Encoder Decoder) which is trained via a novel loss function for predicting the column mask for a given input image. Experiments have been performed to analyze the different components of the proposed pipeline, thus supporting their inclusion for enhanced performance. The proposed pipeline has been evaluated on the challenging ICDAR 2013 table structure recognition dataset, where it demonstrates state-of-the-art performance.

Table Structure Recognition Using CoDec Encoder-Decoder.

Research in face recognition has evolved over the past few decades. With initial research focusing heavily on constrained images, recent research has focused more on unconstrained images captured in-the-wild settings. Faces captured in unconstrained settings with disguise accessories persist to be a challenge for automated face verification. To this effect, this research proposes a novel deep learning framework for disguised face verification. A novel Inverse Disguise Quality metric is proposed for evaluating amount of disguise in the input image, which is utilized in likelihood ratio as a quality score for enhanced verification performance. The proposed framework is model-agnostic and can be applied in conjunction with existing state-of-the-art face verification models for obtaining improved performance. Experiments have been performed on the Disguised Faces in Wild (DFW) 2018 and DFW 2019 datasets, with three state-of-the-art deep learning models, where it demonstrates substantial improvement compared to the base model.

Disguised Face Verification Using Inverse Disguise Quality

Gender is one of the most common attributes used to describe an individual. It is used in multiple domains such as human computer interaction, marketing, security, and demographic reports. Research has been performed to automate the task of gender recognition in constrained environment using face images, however, limited attention has been given to gender classification in unconstrained scenarios. This work attempts to address the challenging problem of gender classification in multi-spectral low resolution face images. We propose a robust Class Representative Autoencoder model, termed as AutoGen for the same. The proposed model aims to minimize the intra-class variations while maximizing the inter-class variations for the learned feature representations. Results on visible as well as near infrared spectrum data for different resolutions and multiple databases depict the efficacy of the proposed model. Comparative results with existing approaches and two commercial off-the-shelf systems further motivate the use of class representative features for classification.

Maneet Singh

Papers

Semi-supervised Learning for Marked Temporal Point Processes.

Editorial Introduction to the Special Issue on Biometrics at a Distance in the Deep Learning Era

Table Structure Recognition Using CoDec Encoder-Decoder.

Disguised Face Verification Using Inverse Disguise Quality

Class Representative Autoencoder for Low Resolution Multi-Spectral Gender Classification