Search Results for author: Enver Sangineto

Found 23 papers, 13 papers with code

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

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.

Unsupervised Image-To-Image Translation

Efficient Training of Visual Transformers with Small-Size Datasets

no code implementations7 Jun 2021 Yahui Liu, Enver Sangineto, Wei Bi, Nicu Sebe, Bruno Lepri, Marco De Nadai

Visual Transformers (VTs) are emerging as an architectural paradigm alternative to Convolutional networks (CNNs).

Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization

1 code implementation31 May 2021 Jichao Zhang, Aliaksandr Siarohin, Hao Tang, Jingjing Chen, Enver Sangineto, Wei Wang, Nicu Sebe

Controllable person image generation aims to produce realistic human images with desirable attributes (e. g., the given pose, cloth textures or hair style).

Image-to-Image Translation Pose Transfer

Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild

1 code implementation9 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.

Online Continual Learning under Extreme Memory Constraints

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.

Continual Learning

Whitening for Self-Supervised Representation Learning

2 code implementations13 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").

Representation Learning Self-Supervised Learning

TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation

no code implementations19 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.

Domain Adaptation Image-to-Image Translation

Coarse-to-Fine Gaze Redirection with Numerical and Pictorial Guidance

1 code implementation7 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.

gaze redirection Image Generation

Attention-based Fusion for Multi-source Human Image Generation

no code implementations7 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.

Image Generation

Whitening and Coloring transform for GANs

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.

Appearance and Pose-Conditioned Human Image Generation using Deformable GANs

1 code implementation30 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.

Data Augmentation Image Generation +1

Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images

1 code implementation2 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.

Metric Learning

Whitening and Coloring batch transform for GANs

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.

Image Generation

FOIL it! Find One mismatch between Image and Language caption

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.

Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection

no code implementations2 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.

Anomaly Detection Event Detection +1

Unsupervised Tube Extraction Using Transductive Learning and Dense Trajectories

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.

Object Detection Optical Flow Estimation

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