1 code implementation • 27 Jan 2023 • Mang Ning, Enver Sangineto, Angelo Porrello, Simone Calderara, Rita Cucchiara
In this paper, we observe that a long sampling chain also leads to an error accumulation phenomenon, which is similar to the \textbf{exposure bias} problem in autoregressive text generation.
Ranked #1 on
Image Generation
on LSUN tower 64x64
no code implementations • 3 Oct 2022 • Yahui Liu, Enver Sangineto, Yajing Chen, Linchao Bao, Haoxian Zhang, Nicu Sebe, Bruno Lepri, Marco De Nadai
Multi-domain image-to-image (I2I) translations can transform a source image according to the style of a target domain.
1 code implementation • 1 Jul 2022 • Jichao Zhang, Jingjing Chen, Hao Tang, Enver Sangineto, Peng Wu, Yan Yan, Nicu Sebe, Wei Wang
Solving this problem using an unsupervised method remains an open problem, especially for high-resolution face images in the wild, which are not easy to annotate with gaze and head pose labels.
no code implementations • 9 Jun 2022 • Elia Peruzzo, Enver Sangineto, Yahui Liu, Marco De Nadai, Wei Bi, Bruno Lepri, Nicu Sebe
In this paper, we explicitly encourage the emergence of this spatial clustering as a form of training regularization, this way including a self-supervised pretext task into the standard supervised learning.
1 code implementation • 7 Apr 2022 • Aleksandr Ermolov, Enver Sangineto, Nicu Sebe
Inspired by human memory, we propose to represent history with only important changes in the environment and, in our approach, to obtain automatically this representation using self-supervision.
1 code implementation • 2 Dec 2021 • Jichao Zhang, Enver Sangineto, Hao Tang, Aliaksandr Siarohin, Zhun Zhong, Nicu Sebe, Wei Wang
However, they usually struggle to generate high-quality images representing non-rigid objects, such as the human body, which is of a great interest for many computer graphics applications.
1 code implementation • ICCV 2021 • Enrico Fini, Enver Sangineto, Stéphane Lathuilière, Zhun Zhong, Moin Nabi, Elisa Ricci
In this paper, we study the problem of Novel Class Discovery (NCD).
no code implementations • CVPR 2021 • Yahui Liu, Enver Sangineto, Yajing Chen, Linchao Bao, Haoxian Zhang, Nicu Sebe, Bruno Lepri, Wei Wang, Marco De Nadai
In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation.
1 code implementation • NeurIPS 2021 • Yahui Liu, Enver Sangineto, Wei Bi, Nicu Sebe, Bruno Lepri, Marco De Nadai
This task encourages the VTs to learn spatial relations within an image and makes the VT training much more robust when training data are scarce.
1 code implementation • 31 May 2021 • Jichao Zhang, Aliaksandr Siarohin, Hao Tang, Enver Sangineto, Wei Wang, Humphrey Sh, Nicu Sebe
Moreover, we propose a novel Self-Training Part Replacement (STPR) strategy to refine the model for the texture-transfer task, which improves the quality of the generated clothes and the preservation ability of non-target regions.
1 code implementation • 9 Aug 2020 • Jichao Zhang, Jingjing Chen, Hao Tang, Wei Wang, Yan Yan, Enver Sangineto, Nicu Sebe
In this paper we address the problem of unsupervised gaze correction in the wild, presenting a solution that works without the need for precise annotations of the gaze angle and the head pose.
1 code implementation • ECCV 2020 • Enrico Fini, Stéphane Lathuilière, Enver Sangineto, Moin Nabi, Elisa Ricci
Continual Learning (CL) aims to develop agents emulating the human ability to sequentially learn new tasks while being able to retain knowledge obtained from past experiences.
8 code implementations • 13 Jul 2020 • Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe
Most of the current self-supervised representation learning (SSL) methods are based on the contrastive loss and the instance-discrimination task, where augmented versions of the same image instance ("positives") are contrasted with instances extracted from other images ("negatives").
no code implementations • 19 Apr 2020 • Subhankar Roy, Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe, Elisa Ricci
In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks.
1 code implementation • 7 Apr 2020 • Jingjing Chen, Jichao Zhang, Enver Sangineto, Jiayuan Fan, Tao Chen, Nicu Sebe
In this paper, we propose to alleviate these problems by means of a novel gaze redirection framework which exploits both a numerical and a pictorial direction guidance, jointly with a coarse-to-fine learning strategy.
no code implementations • 25 Sep 2019 • Aleksandr Ermolov, Enver Sangineto, Nicu Sebe
To address this problem, a possible solution is to provide the agent with information about past observations.
no code implementations • 7 May 2019 • Stéphane Lathuilière, Enver Sangineto, Aliaksandr Siarohin, Nicu Sebe
We present a generalization of the person-image generation task, in which a human image is generated conditioned on a target pose and a set X of source appearance images.
no code implementations • ICLR 2019 • Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe
In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization.
1 code implementation • 30 Apr 2019 • Aliaksandr Siarohin, Stéphane Lathuilière, Enver Sangineto, Nicu Sebe
Specifically, given an image xa of a person and a target pose P(xb), extracted from a different image xb, we synthesize a new image of that person in pose P(xb), while preserving the visual details in xa.
1 code implementation • 2 Apr 2019 • Subhankar Roy, Enver Sangineto, Begüm Demir, Nicu Sebe
Hashing methods have been recently found very effective in retrieval of remote sensing (RS) images due to their computational efficiency and fast search speed.
1 code implementation • CVPR 2019 • Subhankar Roy, Aliaksandr Siarohin, Enver Sangineto, Samuel Rota Bulo, Nicu Sebe, Elisa Ricci
A classifier trained on a dataset seldom works on other datasets obtained under different conditions due to domain shift.
1 code implementation • ICLR 2019 • Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe
In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization.
1 code implementation • CVPR 2018 • Aliaksandr Siarohin, Enver Sangineto, Stephane Lathuiliere, Nicu Sebe
Specifically, given an image of a person and a target pose, we synthesize a new image of that person in the novel pose.
Ranked #5 on
Gesture-to-Gesture Translation
on NTU Hand Digit
Gesture-to-Gesture Translation
Image-to-Image Translation
+1
no code implementations • 31 Aug 2017 • Mahdyar Ravanbakhsh, Moin Nabi, Enver Sangineto, Lucio Marcenaro, Carlo Regazzoni, Nicu Sebe
In this paper we address the abnormality detection problem in crowded scenes.
Ranked #4 on
Abnormal Event Detection In Video
on UCSD Ped2
no code implementations • 23 Jun 2017 • Mahdyar Ravanbakhsh, Enver Sangineto, Moin Nabi, Nicu Sebe
Abnormal crowd behaviour detection attracts a large interest due to its importance in video surveillance scenarios.
no code implementations • ACL 2017 • Ravi Shekhar, Sandro Pezzelle, Yauhen Klimovich, Aurelie Herbelot, Moin Nabi, Enver Sangineto, Raffaella Bernardi
In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities.
no code implementations • 2 Oct 2016 • Mahdyar Ravanbakhsh, Moin Nabi, Hossein Mousavi, Enver Sangineto, Nicu Sebe
In this paper, we show that keeping track of the changes in the CNN feature across time can facilitate capturing the local abnormality.
1 code implementation • 24 May 2016 • Enver Sangineto, Moin Nabi, Dubravko Culibrk, Nicu Sebe
The main idea is to iteratively select a subset of images and boxes that are the most reliable, and use them for training.
Ranked #31 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
1 code implementation • ICCV 2015 • Mihai Marian Puscas, Enver Sangineto, Dubravko Culibrk, Nicu Sebe
The combination of appearance-based static ''objectness'' (Selective Search), motion information (Dense Trajectories) and transductive learning (detectors are forced to "overfit" on the unsupervised data used for training) makes the proposed approach extremely robust.