Search Results for author: Elisa Ricci

Found 62 papers, 33 papers with code

Click to Move: Controlling Video Generation with Sparse Motion

no code implementations19 Aug 2021 Pierfrancesco Ardino, Marco De Nadai, Bruno Lepri, Elisa Ricci, Stéphane Lathuilière

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 simple object trajectories of the key objects in the scene.

Video Generation

Neighborhood Contrastive Learning for Novel Class Discovery

no code implementations CVPR 2021 Zhun Zhong, Enrico Fini, Subhankar Roy, Zhiming Luo, Elisa Ricci, Nicu Sebe

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.

Contrastive Learning

Transformer-Based Source-Free Domain Adaptation

1 code implementation28 May 2021 Guanglei Yang, Hao Tang, Zhun Zhong, Mingli Ding, Ling Shao, Nicu Sebe, Elisa Ricci

In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation.

Domain Adaptation Knowledge Distillation

Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation

1 code implementation CVPR 2021 Subhankar Roy, Evgeny Krivosheev, Zhun Zhong, Nicu Sebe, Elisa Ricci

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 task is to learn a robust predictor for all the target domains.

Curriculum Learning Domain Adaptation +1

Inferring Latent Domains for Unsupervised Deep Domain Adaptation

no code implementations25 Mar 2021 Massimiliano Mancini, Lorenzo Porzi, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci

Most deep UDA approaches operate in a single-source, single-target scenario, i. e. they assume that the source and the target samples arise from a single distribution.

Unsupervised Domain Adaptation

Boosting Binary Masks for Multi-Domain Learning through Affine Transformations

no code implementations25 Mar 2021 Massimiliano Mancini, Elisa Ricci, Barbara Caputo, Samuel Rota Buló

In this work, we provide a general formulation of binary mask based models for multi-domain learning by affine transformations of the original network parameters.

Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction

1 code implementation22 Mar 2021 Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, Elisa Ricci

While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation.

Depth Estimation

Variational Structured Attention Networks for Deep Visual Representation Learning

1 code implementation5 Mar 2021 Guanglei Yang, Paolo Rota, Xavier Alameda-Pineda, Dan Xu, Mingli Ding, Elisa Ricci

Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework, leading to Variational STructured Attention networks (VISTA-Net).

Depth Estimation Representation Learning +1

Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction

no code implementations8 Jan 2021 Dan Xu, Xavier Alameda-Pineda, Wanli Ouyang, Elisa Ricci, Xiaogang Wang, Nicu Sebe

In contrast to previous works directly considering multi-scale feature maps obtained from the inner layers of a primary CNN architecture, and simply fusing the features with weighted averaging or concatenation, we propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs) model for learning and fusing multi-scale representations in a principled manner.

BSDS500 Graph Attention +2

Variational Structured Attention Networks for Dense Pixel-Wise Prediction

no code implementations1 Jan 2021 Guanglei Yang, Paolo Rota, Xavier Alameda-Pineda, Dan Xu, Mingli Ding, Elisa Ricci

State-of-the-art performances in dense pixel-wise prediction tasks are obtained with specifically designed convolutional networks.

Are we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection?

no code implementations10 Dec 2020 Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Peter Kontschieder, Elisa Ricci

Pseudo-LiDAR-based methods for monocular 3D object detection have received considerable attention in the community due to the performance gains exhibited on the KITTI3D benchmark, in particular on the commonly reported validation split.

Monocular 3D Object Detection

Semantic-Guided Inpainting Network for Complex Urban Scenes Manipulation

1 code implementation19 Oct 2020 Pierfrancesco Ardino, Yahui Liu, Elisa Ricci, Bruno Lepri, Marco De Nadai

Inspired by recent works on image inpainting, our proposed method leverages the semantic segmentation to model the content and structure of the image, and learn the best shape and location of the object to insert.

Image Inpainting Semantic Segmentation

SF-UDA$^{3D}$: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection

1 code implementation16 Oct 2020 Cristiano Saltori, Stéphane Lathuiliére, Nicu Sebe, Elisa Ricci, Fabio Galasso

In the case of LiDAR, in fact, domain shift is not only due to changes in the environment and in the object appearances, as for visual data from RGB cameras, but is also related to the geometry of the point clouds (e. g., point density variations).

3D Object Detection Unsupervised Domain Adaptation

Deep Variational Generative Models for Audio-visual Speech Separation

no code implementations17 Aug 2020 Viet-Nhat Nguyen, Mostafa Sadeghi, Elisa Ricci, Xavier Alameda-Pineda

To better utilize the visual information, the posteriors of the latent variables are inferred from mixed speech (instead of clean speech) as well as the visual data.

Speech Separation

Learning to Cluster under Domain Shift

1 code implementation ECCV 2020 Willi Menapace, Stéphane Lathuilière, Elisa Ricci

While unsupervised domain adaptation methods based on deep architectures have achieved remarkable success in many computer vision tasks, they rely on a strong assumption, i. e. labeled source data must be available.

Deep Clustering Unsupervised Domain Adaptation

Shape Consistent 2D Keypoint Estimation under Domain Shift

no code implementations4 Aug 2020 Levi O. Vasconcelos, Massimiliano Mancini, Davide Boscaini, Samuel Rota Bulo, Barbara Caputo, Elisa Ricci

Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex problems involving structured predictions (e. g. semantic segmentation, depth estimation).

Depth Estimation Semantic Segmentation +1

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

Towards Recognizing Unseen Categories in Unseen Domains

1 code implementation ECCV 2020 Massimiliano Mancini, Zeynep Akata, Elisa Ricci, Barbara Caputo

The key idea of CuMix is to simulate the test-time domain and semantic shift using images and features from unseen domains and categories generated by mixing up the multiple source domains and categories available during training.

Domain Generalization Zero-Shot Learning +1

Boosting Deep Open World Recognition by Clustering

no code implementations20 Apr 2020 Dario Fontanel, Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, Barbara Caputo

While convolutional neural networks have brought significant advances in robot vision, their ability is often limited to closed world scenarios, where the number of semantic concepts to be recognized is determined by the available training set.

Incremental Learning Open Set 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

Motion-supervised Co-Part Segmentation

1 code implementation7 Apr 2020 Aliaksandr Siarohin, Subhankar Roy, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, Nicu Sebe

To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation.

Modeling the Background for Incremental Learning in Semantic Segmentation

1 code implementation CVPR 2020 Fabio Cermelli, Massimiliano Mancini, Samuel Rota Bulò, Elisa Ricci, Barbara Caputo

Current strategies fail on this task because they do not consider a peculiar aspect of semantic segmentation: since each training step provides annotation only for a subset of all possible classes, pixels of the background class (i. e. pixels that do not belong to any other classes) exhibit a semantic distribution shift.

Continual Learning Incremental Learning +1

Towards Generalization Across Depth for Monocular 3D Object Detection

no code implementations ECCV 2020 Andrea Simonelli, Samuel Rota Bulò, Lorenzo Porzi, Elisa Ricci, Peter Kontschieder

While expensive LiDAR and stereo camera rigs have enabled the development of successful 3D object detection methods, monocular RGB-only approaches lag much behind.

Monocular 3D Object Detection

Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks

1 code implementation17 Sep 2019 Andrea Pilzer, Stéphane Lathuilière, Dan Xu, Mihai Marian Puscas, Elisa Ricci, Nicu Sebe

Extensive experiments on the publicly available datasets KITTI, Cityscapes and ApolloScape demonstrate the effectiveness of the proposed model which is competitive with other unsupervised deep learning methods for depth prediction.

Data Augmentation Monocular Depth Estimation +1

Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition

no code implementations4 Jun 2019 Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci, Patric Jensfelt, Barbara Caputo

While today's robots are able to perform sophisticated tasks, they can only act on objects they have been trained to recognize.

Open Set Learning

Budget-Aware Adapters for Multi-Domain Learning

no code implementations ICCV 2019 Rodrigo Berriel, Stéphane Lathuilière, Moin Nabi, Tassilo Klein, Thiago Oliveira-Santos, Nicu Sebe, Elisa Ricci

To implement this idea we derive specialized deep models for each domain by adapting a pre-trained architecture but, differently from other methods, we propose a novel strategy to automatically adjust the computational complexity of the network.

Online Adaptation through Meta-Learning for Stereo Depth Estimation

no code implementations17 Apr 2019 Zhen-Yu Zhang, Stéphane Lathuilière, Andrea Pilzer, Nicu Sebe, Elisa Ricci, Jian Yang

Our proposal is evaluated on the wellestablished KITTI dataset, where we show that our online method is competitive withstate of the art algorithms trained in a batch setting.

Meta-Learning Stereo Depth Estimation

The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots

1 code implementation1 Apr 2019 Fabio Cermelli, Massimiliano Mancini, Elisa Ricci, Barbara Caputo

Deep networks have brought significant advances in robot perception, enabling to improve the capabilities of robots in several visual tasks, ranging from object detection and recognition to pose estimation, semantic scene segmentation and many others.

Object Detection Pose Estimation +1

AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs

1 code implementation CVPR 2019 Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci

The ability to categorize is a cornerstone of visual intelligence, and a key functionality for artificial, autonomous visual machines.

Domain Adaptation

Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation

no code implementations CVPR 2019 Andrea Pilzer, Stéphane Lathuilière, Nicu Sebe, Elisa Ricci

Therefore, recent works have proposed deep architectures for addressing the monocular depth prediction task as a reconstruction problem, thus avoiding the need of collecting ground-truth depth.

Knowledge Distillation Monocular Depth Estimation

Enhancing Perceptual Attributes with Bayesian Style Generation

1 code implementation3 Dec 2018 Aliaksandr Siarohin, Gloria Zen, Nicu Sebe, Elisa Ricci

Our approach takes as input a natural image and exploits recent models for deep style transfer and generative adversarial networks to change its style in order to modify a specific high-level attribute.

Style Transfer

Unsupervised Adversarial Depth Estimation using Cycled Generative Networks

2 code implementations28 Jul 2018 Andrea Pilzer, Dan Xu, Mihai Marian Puscas, Elisa Ricci, Nicu Sebe

The proposed architecture consists of two generative sub-networks jointly trained with adversarial learning for reconstructing the disparity map and organized in a cycle such as to provide mutual constraints and supervision to each other.

Monocular Depth Estimation

Kitting in the Wild through Online Domain Adaptation

no code implementations3 Jul 2018 Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci, Patric Jensfelt, Barbara Caputo

This novel dataset allows for testing the robustness of robot visual recognition algorithms to a series of different domain shifts both in isolation and unified.

Domain Adaptation Object Recognition

Best sources forward: domain generalization through source-specific nets

no code implementations15 Jun 2018 Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci

A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions.

Domain Generalization

Robust Place Categorization with Deep Domain Generalization

1 code implementation30 May 2018 Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci

Our method develops from the intuition that, given a set of different classification models associated to known domains (e. g. corresponding to multiple environments, robots), the best model for a new sample in the novel domain can be computed directly at test time by optimally combining the known models.

Domain Generalization General Classification

Boosting Domain Adaptation by Discovering Latent Domains

2 code implementations CVPR 2018 Massimiliano Mancini, Lorenzo Porzi, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci

Our approach is based on the introduction of two main components, which can be embedded into any existing CNN architecture: (i) a side branch that automatically computes the assignment of a source sample to a latent domain and (ii) novel layers that exploit domain membership information to appropriately align the distribution of the CNN internal feature representations to a reference distribution.

Domain Adaptation

Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation

1 code implementation CVPR 2018 Dan Xu, Wei Wang, Hao Tang, Hong Liu, Nicu Sebe, Elisa Ricci

Recent works have shown the benefit of integrating Conditional Random Fields (CRFs) models into deep architectures for improving pixel-level prediction tasks.

Monocular Depth Estimation

Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction

no code implementations NeurIPS 2017 Dan Xu, Wanli Ouyang, Xavier Alameda-Pineda, Elisa Ricci, Xiaogang Wang, Nicu Sebe

Recent works have shown that exploiting multi-scale representations deeply learned via convolutional neural networks (CNN) is of tremendous importance for accurate contour detection.

BSDS500 Contour Detection

AutoDIAL: Automatic DomaIn Alignment Layers

1 code implementation ICCV 2017 Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, Samuel Rota Bulò

Here we take a different route, proposing to align the learned representations by embedding in any given network specific Domain Alignment Layers, designed to match the source and target feature distributions to a reference one.

Domain Adaptation

Learning Cross-Modal Deep Representations for Robust Pedestrian Detection

2 code implementations CVPR 2017 Dan Xu, Wanli Ouyang, Elisa Ricci, Xiaogang Wang, Nicu Sebe

Then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the detection results.

Pedestrian Detection

How to Make an Image More Memorable? A Deep Style Transfer Approach

1 code implementation6 Apr 2017 Aliaksandr Siarohin, Gloria Zen, Cveta Majtanovic, Xavier Alameda-Pineda, Elisa Ricci, Nicu Sebe

In this work, we show that it is possible to automatically retrieve the best style seeds for a given image, thus remarkably reducing the number of human attempts needed to find a good match.

Image Generation Style Transfer

Viraliency: Pooling Local Virality

1 code implementation CVPR 2017 Xavier Alameda-Pineda, Andrea Pilzer, Dan Xu, Nicu Sebe, Elisa Ricci

In our overly-connected world, the automatic recognition of virality - the quality of an image or video to be rapidly and widely spread in social networks - is of crucial importance, and has recently awaken the interest of the computer vision community.

Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation

no code implementations21 Feb 2017 Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, Samuel Rota Bulò

The empirical fact that classifiers, trained on given data collections, perform poorly when tested on data acquired in different settings is theoretically explained in domain adaptation through a shift among distributions of the source and target domains.

Unsupervised Domain Adaptation

Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions

no code implementations CVPR 2016 Sergey Tulyakov, Xavier Alameda-Pineda, Elisa Ricci, Lijun Yin, Jeffrey F. Cohn, Nicu Sebe

Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR).

Heart rate estimation Matrix Completion

Uncovering Interactions and Interactors: Joint Estimation of Head, Body Orientation and F-Formations From Surveillance Videos

no code implementations ICCV 2015 Elisa Ricci, Jagannadan Varadarajan, Ramanathan Subramanian, Samuel Rota Bulo, Narendra Ahuja, Oswald Lanz

We present a novel approach for jointly estimating tar- gets' head, body orientations and conversational groups called F-formations from a distant social scene (e. g., a cocktail party captured by surveillance cameras).

Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

no code implementations6 Oct 2015 Dan Xu, Elisa Ricci, Yan Yan, Jingkuan Song, Nicu Sebe

We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes.

Anomaly Detection Denoising +1

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

no code implementations23 Jun 2015 Xavier Alameda-Pineda, Jacopo Staiano, Ramanathan Subramanian, Ligia Batrinca, Elisa Ricci, Bruno Lepri, Oswald Lanz, Nicu Sebe

Studying free-standing conversational groups (FCGs) in unstructured social settings (e. g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels.

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