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20152023
most citedLearning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction

103 citations · 309 across the 21 of their papers we have counts for

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Showing 2021Show all

10 papers · 1 filter

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.CV202113 cited

Neighborhood Contrastive Learning for Novel Class Discovery

Zhun Zhong, Enrico Fini, Subhankar Roy +3

In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes. We exploit the pe…

cs.CV202121 cited

Transformer-Based Source-Free Domain Adaptation

Guanglei Yang, Hao Tang, Zhun Zhong +4

In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation. Previous works on SFDA mainly focus on a…

cs.CV2021

Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation

Subhankar Roy, Evgeny Krivosheev, Zhun Zhong +2

In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the…

cs.CV20218 cited

Boosting Binary Masks for Multi-Domain Learning through Affine Transformations

Massimiliano Mancini, Elisa Ricci, Barbara Caputo +1

In this work, we present a new, algorithm for multi-domain learning. Given a pretrained architecture and a set of visual domains received sequentially, the goal of multi-domain lea…