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Regularization by denoising (RED) is an image reconstruction framework that uses an image denoiser as a prior. Recent work has shown the state-of-the-art performance of RED with learned denoisers corresponding to pre-trained convolutional neural nets (CNNs). In this work, we propose to broaden the current denoiser-centric view of RED by considering priors corresponding to networks trained for more general artifact-removal.
One challenging aspect in face anti-spoofing (or presentation attack detection, PAD) refers to the difficulty of collecting enough and representative attack samples for an application-specific environment. In view of this, we tackle the problem of training a robust PAD model with limited data in an application-specific domain.
With the rapid progress in recent years, techniques that generate and manipulate multimedia content can now provide a very advanced level of realism. The boundary between real and synthetic media has become very thin. On the one hand, this opens the door to a series of exciting applications in different fields such as creative arts, advertising, film production, and video games. On the other hand, it poses enormous security threats. Software packages freely available on the web allow any individual, without special skills, to create very realistic fake images and videos.
Lecture Date: December 7, 2020 (Virtual Lecture)
Chapter: Beijing
Chapter Chair: Qiuqi Ruan
Topic: Binary optimizations in signal processing
Date: October 28, 2020
Time: 11:00 AM EDT (New York Time)
Title: Joint Optimization of Radio and Computational
Resources in Mobile Edge Computing
Registration | Full webinar details
White Paper Due: November 1, 2020
Publication Date: November 2021
CFP Document
White Paper Due: February 1, 2021
Publication Date: March 2022
CFP Document
Submission Deadline: February 19, 2021
Call for Proposals Document
Position description: The research project will focus on developing machine learning/deep learning methods for fundamental computer vision problems including object motion tracking, segmentation, 3D reconstruction, classification and image captioning in 2D/3D images including RGBD images, remote sensing data, 3D CT/MRI medical images and biomedical text.