activity
20142019
most citedAre Safer Looking Neighborhoods More Lively? A Multimodal Investigation into Urban Life

18 citations · 50 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2019★ 16 cited

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

Yahui Liu, Marco De Nadai, Gloria Zen +2

Recent works have shown Generative Adversarial Networks (GANs) to be particularly effective in image-to-image translations. However, in tasks such as body pose and hand gesture tra…

stat.ML2018

Enhancing Perceptual Attributes with Bayesian Style Generation

Aliaksandr Siarohin, Gloria Zen, Nicu Sebe +1

Deep learning has brought an unprecedented progress in computer vision and significant advances have been made in predicting subjective properties inherent to visual data (e.g., me…

cs.CV2017★ 7 cited

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

Aliaksandr Siarohin, Gloria Zen, Cveta Majtanovic +3

Recent works have shown that it is possible to automatically predict intrinsic image properties like memorability. In this paper, we take a step forward addressing the question: "C…

cs.CY2016★ 18 cited

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

Marco De Nadai, Radu L. Vieriu, Gloria Zen +6

Policy makers, urban planners, architects, sociologists, and economists are interested in creating urban areas that are both lively and safe. But are the safety and liveliness of n…

cs.CV2014★ 9 cited

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

Adrien Gaidon, Gloria Zen, Jose A. Rodriguez-Serrano

Learning object detectors requires massive amounts of labeled training samples from the specific data source of interest. This is impractical when dealing with many different sourc…