activity
20172022
most citedOnline Adaptation through Meta-Learning for Stereo Depth Estimation

13 citations · 16 across the 6 of their papers we have counts for

collaborators

18 papers

cs.CV20221 cited

Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation

Yangsong Zhang, Subhankar Roy, Hongtao Lu +2

In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unann…

cs.CV20221 cited

Playable Environments: Video Manipulation in Space and Time

Willi Menapace, Stéphane Lathuilière, Aliaksandr Siarohin +4

We present Playable Environments - a new representation for interactive video generation and manipulation in space and time. With a single image at inference time, our novel framew…

cs.CV2021

A Unified Objective for Novel Class Discovery

Enrico Fini, Enver Sangineto, Stéphane Lathuilière +3

In this paper, we study the problem of Novel Class Discovery (NCD). NCD aims at inferring novel object categories in an unlabeled set by leveraging from prior knowledge of a labele…

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

Playable Video Generation

Willi Menapace, Stéphane Lathuilière, Sergey Tulyakov +2

This paper introduces the unsupervised learning problem of playable video generation (PVG). In PVG, we aim at allowing a user to control the generated video by selecting a discrete…

cs.CV2021

Multi-Domain Image-to-Image Translation with Adaptive Inference Graph

The-Phuc Nguyen, Stéphane Lathuilière, Elisa Ricci

In this work, we address the problem of multi-domain image-to-image translation with particular attention paid to computational cost. In particular, current state of the art models…