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Marco De Nadai

FBK

21 papers hereh-index 162.3k citations39 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author6
  • middle author10
  • last author5

Across the 21 of 21 papers where every author was matched, so the position is known.

fields
  • cs.CV11
  • cs.CY7
  • physics.soc-ph2
  • cs.SI1
affiliations
  • FBK
Homepage
same name
  • Marco De Nadai — 5 papers, h 3
  • Marco De Nadai — 3 papers, h 4
  • Marco De Nadai — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162024
most citedAre Safer Looking Neighborhoods More Lively? A Multimodal Investigation into Urban Life

18 citations · 29 across the 10 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CV2021

ISF-GAN: An Implicit Style Function for High-Resolution Image-to-Image Translation

Yahui Liu, Yajing Chen, Linchao Bao +3

Recently, there has been an increasing interest in image editing methods that employ pre-trained unconditional image generators (e.g., StyleGAN). However, applying these methods to…

cs.CV2021

Click to Move: Controlling Video Generation with Sparse Motion

Pierfrancesco Ardino, Marco De Nadai, Bruno Lepri +2

This paper introduces Click to Move (C2M), a novel framework for video generation where the user can control the motion of the synthesized video through mouse clicks specifying sim…

cs.CV2021

Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation

Yahui Liu, Enver Sangineto, Yajing Chen +6

Image-to-Image (I2I) multi-domain translation models are usually evaluated also using the quality of their semantic interpolation results. However, state-of-the-art models frequent…

cs.CV2021

Efficient Training of Visual Transformers with Small Datasets

Yahui Liu, Enver Sangineto, Wei Bi +3

Visual Transformers (VTs) are emerging as an architectural paradigm alternative to Convolutional networks (CNNs). Differently from CNNs, VTs can capture global relations between im…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.