Search Results for author: Gloria Zen

Found 5 papers, 4 papers with code

Gesture-to-Gesture Translation in the Wild via Category-Independent Conditional Maps

1 code implementation12 Jul 2019 Yahui Liu, Marco De Nadai, Gloria Zen, Nicu Sebe, Bruno Lepri

In this work, we propose a novel GAN architecture that decouples the required annotations into a category label - that specifies the gesture type - and a simple-to-draw category-independent conditional map - that expresses the location, rotation and size of the hand gesture.

Gesture-to-Gesture Translation Translation

Enhancing Perceptual Attributes with Bayesian Style Generation

1 code implementation3 Dec 2018 Aliaksandr Siarohin, Gloria Zen, Nicu Sebe, Elisa Ricci

Our approach takes as input a natural image and exploits recent models for deep style transfer and generative adversarial networks to change its style in order to modify a specific high-level attribute.

Attribute Style Transfer

How to Make an Image More Memorable? A Deep Style Transfer Approach

1 code implementation6 Apr 2017 Aliaksandr Siarohin, Gloria Zen, Cveta Majtanovic, Xavier Alameda-Pineda, Elisa Ricci, Nicu Sebe

In this work, we show that it is possible to automatically retrieve the best style seeds for a given image, thus remarkably reducing the number of human attempts needed to find a good match.

Image Generation Style Transfer

Are Safer Looking Neighborhoods More Lively? A Multimodal Investigation into Urban Life

1 code implementation1 Aug 2016 Marco De Nadai, Radu L. Vieriu, Gloria Zen, Stefan Dragicevic, Nikhil Naik, Michele Caraviello, Cesar A. Hidalgo, Nicu Sebe, Bruno Lepri

But in a world where the preference for safe looking neighborhoods is small, the connection between the perception of safety and liveliness will be either weak or nonexistent.

Computers and Society Social and Information Networks Physics and Society

Self-Learning Camera: Autonomous Adaptation of Object Detectors to Unlabeled Video Streams

no code implementations17 Jun 2014 Adrien Gaidon, Gloria Zen, Jose A. Rodriguez-Serrano

In this paper, we address the problem of self-learning detectors in an autonomous manner, i. e. (i) detectors continuously updating themselves to efficiently adapt to streaming data sources (contrary to transductive algorithms), (ii) without any labeled data strongly related to the target data stream (contrary to self-paced learning), and (iii) without manual intervention to set and update hyper-parameters.

Multi-Task Learning Object +1

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