Titlebook: Computer Vision – ECCV 2020 Workshops; Glasgow, UK, August Adrien Bartoli,Andrea Fusiello Conference proceedings 2020 Springer Nature Swit

四指套

A Benchmark for Inpainting of Clothing Images with Irregular Holeson intelligent fashion analysis systems, clothing image inpainting has not been extensively examined yet. For that matter, we present an extensive benchmark of clothing image inpainting on well-known fashion datasets. Furthermore, we introduce the use of a dilated version of partial convolutions, wh

壕溝

Learning to Improve Image Compression Without Changing the Standard Decoderompression. However, the existing approaches either train a post-processing DNN on the decoder side, or propose learning for image compression in an end-to-end manner. This way, the trained DNNs are required in the decoder, leading to the incompatibility to the standard image decoders (., JPEG) in p

lattice

Conditional Adversarial Camera Model Anonymizationfic artifacts present within the image. Model anonymization is the process of transforming these artifacts such that the apparent capture model is changed. We propose a conditional adversarial approach for learning such transformations. In contrast to previous works, we cast model anonymization as t

施舍

Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial rate new images of that same person under different expressions and poses. Some systems can also modify targeted attributes such as hair color or age. This type of manipulated images and video have been coined Deepfakes. In order to prevent a malicious user from generating modified images of a perso

易發(fā)怒

Efficiently Detecting Plausible Locations for Object Placement Using Masked Convolutions manipulation, the plausible placement and the blending of the new objects in the image are critical. In this paper, we propose a fast method for the automatic selection of plausible locations for object insertion into images. Like previous work, we approach the object placement problem as a detecti

habitat

L2-Constrained RemNet for Camera Model Identification and Image Manipulation Detection L2-constrained Remnant Convolutional Neural Network (L2-constrained RemNet) for performing these two crucial tasks. The proposed network architecture consists of a dynamic preprocessor block and a classification block. An L2 loss is applied to the output of the preprocessor block, and categorical c

ENACT

四指套

Dungeon

Participation and the Nature of the Firm,l attribution classifier, which constrains the generative network to transform the full range of artifacts. Quantitative comparisons demonstrate the efficacy of our framework in a restrictive non-interactive black-box setting.

cancellous-bone

A Benchmark for Inpainting of Clothing Images with Irregular Holesrent masks. Experiments show that dilated partial convolutions (DPConv) improve the quantitative inpainting performance when compared to the other inpainting strategies, especially it performs better when the mask size is 20% or more of the image.