activity
20142026
most citedSocializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval

122 citations · 483 across the 70 of their papers we have counts for

collaborators
Showing 2017Show all

7 papers · 1 filter

cs.CV2017★ 31 cited

Memory Based Online Learning of Deep Representations from Video Streams

Federico Pernici, Federico Bartoli, Matteo Bruni +1

We present a novel online unsupervised method for face identity learning from video streams. The method exploits deep face descriptors together with a memory based learning mechani…

cs.CV2017

Unsupervised Incremental Learning of Deep Descriptors From Video Streams

Federico Pernici, Alberto Del Bimbo

We present a novel unsupervised method for face identity learning from video sequences. The method exploits the ResNet deep network for face detection and VGGface fc7 face descript…

cs.CV2017

Group Re-Identification via Unsupervised Transfer of Sparse Features Encoding

Giuseppe Lisanti, Niki Martinel, Alberto Del Bimbo +1

Person re-identification is best known as the problem of associating a single person that is observed from one or more disjoint cameras. The existing literature has mainly addresse…

cs.CV2017★ 2 cited

Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices

Mohamed Daoudi, Stefano Berretti, Pietro Pala +2

Emotion recognition is attracting great interest for its potential application in a multitude of real-life situations. Much of the Computer Vision research in this field has focuse…

cs.CV2017

Context-Aware Trajectory Prediction

Federico Bartoli, Giuseppe Lisanti, Lamberto Ballan +1

Human motion and behaviour in crowded spaces is influenced by several factors, such as the dynamics of other moving agents in the scene, as well as the static elements that might b…

cs.CV2017

Am I Done? Predicting Action Progress in Videos

Federico Becattini, Tiberio Uricchio, Lorenzo Seidenari +2

In this paper we deal with the problem of predicting action progress in videos. We argue that this is an extremely important task since it can be valuable for a wide range of inter…