Search Results for author: Elisa Ricci

Found 112 papers, 75 papers with code

Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis

no code implementations22 Feb 2024 Willi Menapace, Aliaksandr Siarohin, Ivan Skorokhodov, Ekaterina Deyneka, Tsai-Shien Chen, Anil Kag, Yuwei Fang, Aleksei Stoliar, Elisa Ricci, Jian Ren, Sergey Tulyakov

Since video content is highly redundant, we argue that naively bringing advances of image models to the video generation domain reduces motion fidelity, visual quality and impairs scalability.

Image Generation Text-to-Video Generation +1

Democratizing Fine-grained Visual Recognition with Large Language Models

no code implementations24 Jan 2024 Mingxuan Liu, Subhankar Roy, Wenjing Li, Zhun Zhong, Nicu Sebe, Elisa Ricci

Identifying subordinate-level categories from images is a longstanding task in computer vision and is referred to as fine-grained visual recognition (FGVR).

Fine-Grained Visual Recognition World Knowledge

Incremental Object-Based Novelty Detection with Feedback Loop

no code implementations15 Nov 2023 Simone Caldarella, Elisa Ricci, Rahaf Aljundi

Object-based Novelty Detection (ND) aims to identify unknown objects that do not belong to classes seen during training by an object detection model.

Novelty Detection Object +2

Delving into CLIP latent space for Video Anomaly Recognition

1 code implementation4 Oct 2023 Luca Zanella, Benedetta Liberatori, Willi Menapace, Fabio Poiesi, Yiming Wang, Elisa Ricci

We tackle the complex problem of detecting and recognising anomalies in surveillance videos at the frame level, utilising only video-level supervision.

Anomaly Detection Multiple Instance Learning +1

Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation

1 code implementation28 Aug 2023 Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni, Nicu Sebe, Fabio Poiesi, Elisa Ricci

In this study, we introduce compositional semantic mixing for point cloud domain adaptation, representing the first unsupervised domain adaptation technique for point cloud segmentation based on semantic and geometric sample mixing.

Point Cloud Completion Point Cloud Segmentation +2

On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers

1 code implementation18 Aug 2023 Thomas De Min, Massimiliano Mancini, Karteek Alahari, Xavier Alameda-Pineda, Elisa Ricci

State-of-the-art rehearsal-free continual learning methods exploit the peculiarities of Vision Transformers to learn task-specific prompts, drastically reducing catastrophic forgetting.

Continual Learning Transfer Learning

The Unreasonable Effectiveness of Large Language-Vision Models for Source-free Video Domain Adaptation

1 code implementation ICCV 2023 Giacomo Zara, Alessandro Conti, Subhankar Roy, Stéphane Lathuilière, Paolo Rota, Elisa Ricci

Source-Free Video Unsupervised Domain Adaptation (SFVUDA) task consists in adapting an action recognition model, trained on a labelled source dataset, to an unlabelled target dataset, without accessing the actual source data.

Action Recognition Unsupervised Domain Adaptation

Interactive Neural Painting

no code implementations31 Jul 2023 Elia Peruzzo, Willi Menapace, Vidit Goel, Federica Arrigoni, Hao Tang, Xingqian Xu, Arman Chopikyan, Nikita Orlov, Yuxiao Hu, Humphrey Shi, Nicu Sebe, Elisa Ricci

This paper advances the state of the art in this emerging research domain by proposing the first approach for Interactive NP.

Object-aware Gaze Target Detection

2 code implementations ICCV 2023 Francesco Tonini, Nicola Dall'Asen, Cigdem Beyan, Elisa Ricci

Gaze target detection aims to predict the image location where the person is looking and the probability that a gaze is out of the scene.


Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representations

1 code implementation4 Jul 2023 Anil Osman Tur, Nicola Dall'Asen, Cigdem Beyan, Elisa Ricci

This paper aims to address the unsupervised video anomaly detection (VAD) problem, which involves classifying each frame in a video as normal or abnormal, without any access to labels.

Anomaly Detection Video Anomaly Detection

Vocabulary-free Image Classification

1 code implementation NeurIPS 2023 Alessandro Conti, Enrico Fini, Massimiliano Mancini, Paolo Rota, Yiming Wang, Elisa Ricci

We thus formalize a novel task, termed as Vocabulary-free Image Classification (VIC), where we aim to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary.

Classification Image Classification +4

Rotation Synchronization via Deep Matrix Factorization

1 code implementation9 May 2023 Gk Tejus, Giacomo Zara, Paolo Rota, Andrea Fusiello, Elisa Ricci, Federica Arrigoni

In this paper we address the rotation synchronization problem, where the objective is to recover absolute rotations starting from pairwise ones, where the unknowns and the measures are represented as nodes and edges of a graph, respectively.

Matrix Completion Simultaneous Localization and Mapping

Exploring Diffusion Models for Unsupervised Video Anomaly Detection

no code implementations12 Apr 2023 Anil Osman Tur, Nicola Dall'Asen, Cigdem Beyan, Elisa Ricci

This paper investigates the performance of diffusion models for video anomaly detection (VAD) within the most challenging but also the most operational scenario in which the data annotations are not used.

Anomaly Detection Video Anomaly Detection

AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation

1 code implementation CVPR 2023 Giacomo Zara, Subhankar Roy, Paolo Rota, Elisa Ricci

Open-set Unsupervised Video Domain Adaptation (OUVDA) deals with the task of adapting an action recognition model from a labelled source domain to an unlabelled target domain that contains "target-private" categories, which are present in the target but absent in the source.

Action Recognition Domain Adaptation +1

Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class Discovery

1 code implementation28 Mar 2023 Mingxuan Liu, Subhankar Roy, Zhun Zhong, Nicu Sebe, Elisa Ricci

Discovering novel concepts from unlabelled data and in a continuous manner is an important desideratum of lifelong learners.

Novel Class Discovery Novel Concepts

Promptable Game Models: Text-Guided Game Simulation via Masked Diffusion Models

no code implementations23 Mar 2023 Willi Menapace, Aliaksandr Siarohin, Stéphane Lathuilière, Panos Achlioptas, Vladislav Golyanik, Sergey Tulyakov, Elisa Ricci

Most captivatingly, our PGM unlocks the director's mode, where the game is played by specifying goals for the agents in the form of a prompt.


StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalization

no code implementations18 Feb 2023 Shirsha Bose, Ankit Jha, Enrico Fini, Mainak Singha, Elisa Ricci, Biplab Banerjee

Our method focuses on a domain-agnostic prompt learning strategy, aiming to disentangle the visual style and content information embedded in CLIP's pre-trained vision encoder, enabling effortless adaptation to novel domains during inference.

Domain Generalization Zero-shot Generalization

Simplifying Open-Set Video Domain Adaptation with Contrastive Learning

1 code implementation9 Jan 2023 Giacomo Zara, Victor Guilherme Turrisi da Costa, Subhankar Roy, Paolo Rota, Elisa Ricci

In this work we address a more realistic scenario, called open-set video domain adaptation (OUVDA), where the target dataset contains "unknown" semantic categories that are not shared with the source.

Action Recognition Contrastive Learning +1

A soft nearest-neighbor framework for continual semi-supervised learning

1 code implementation ICCV 2023 Zhiqi Kang, Enrico Fini, Moin Nabi, Elisa Ricci, Karteek Alahari

Despite significant advances, the performance of state-of-the-art continual learning approaches hinges on the unrealistic scenario of fully labeled data.

Continual Learning

ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing

1 code implementation20 Oct 2022 Giulio Mattolin, Luca Zanella, Elisa Ricci, Yiming Wang

Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model trained on a source domain to detect instances from a new target domain for which annotations are not available.

Object Detection Unsupervised Domain Adaptation

Overlap-guided Gaussian Mixture Models for Point Cloud Registration

1 code implementation17 Oct 2022 Guofeng Mei, Fabio Poiesi, Cristiano Saltori, Jian Zhang, Elisa Ricci, Nicu Sebe

Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations.

Point Cloud Registration

Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding

1 code implementation6 Oct 2022 Guofeng Mei, Cristiano Saltori, Fabio Poiesi, Jian Zhang, Elisa Ricci, Nicu Sebe, Qiang Wu

Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.

3D Object Classification Contrastive Learning +3

Cooperative Self-Training for Multi-Target Adaptive Semantic Segmentation

1 code implementation4 Oct 2022 Yangsong Zhang, Subhankar Roy, Hongtao Lu, Elisa Ricci, Stéphane Lathuilière

In this work we address multi-target domain adaptation (MTDA) in semantic segmentation, which consists in adapting a single model from an annotated source dataset to multiple unannotated target datasets that differ in their underlying data distributions.

Domain Adaptation Multi-target Domain Adaptation +2

Multimodal Across Domains Gaze Target Detection

1 code implementation23 Aug 2022 Francesco Tonini, Cigdem Beyan, Elisa Ricci

This paper addresses the gaze target detection problem in single images captured from the third-person perspective.

Gaze Estimation Gaze Target Estimation

Uncertainty-guided Source-free Domain Adaptation

1 code implementation16 Aug 2022 Subhankar Roy, Martin Trapp, Andrea Pilzer, Juho Kannala, Nicu Sebe, Elisa Ricci, Arno Solin

Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model.

Source-Free Domain Adaptation

CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation

2 code implementations20 Jul 2022 Cristiano Saltori, Fabio Galasso, Giuseppe Fiameni, Nicu Sebe, Elisa Ricci, Fabio Poiesi

We propose a new approach of sample mixing for point cloud UDA, namely Compositional Semantic Mix (CoSMix), the first UDA approach for point cloud segmentation based on sample mixing.

3D Unsupervised Domain Adaptation Autonomous Driving +5

Class-incremental Novel Class Discovery

1 code implementation18 Jul 2022 Subhankar Roy, Mingxuan Liu, Zhun Zhong, Nicu Sebe, Elisa Ricci

We study the new task of class-incremental Novel Class Discovery (class-iNCD), which refers to the problem of discovering novel categories in an unlabelled data set by leveraging a pre-trained model that has been trained on a labelled data set containing disjoint yet related categories.

Incremental Learning Knowledge Distillation +1

Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation

1 code implementation26 Mar 2022 Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Moin Nabi, Xavier Alameda-Pineda, Elisa Ricci

This problem has been widely investigated in the research community and several Incremental Learning (IL) approaches have been proposed in the past years.

Contrastive Learning Image Classification +5

Quantum Motion Segmentation

no code implementations24 Mar 2022 Federica Arrigoni, Willi Menapace, Marcel Seelbach Benkner, Elisa Ricci, Vladislav Golyanik

Motion segmentation is a challenging problem that seeks to identify independent motions in two or several input images.

Motion Segmentation Segmentation

3D Object Detection from Images for Autonomous Driving: A Survey

1 code implementation7 Feb 2022 Xinzhu Ma, Wanli Ouyang, Andrea Simonelli, Elisa Ricci

3D object detection from images, one of the fundamental and challenging problems in autonomous driving, has received increasing attention from both industry and academia in recent years.

3D Object Detection Autonomous Driving +1

Continual Attentive Fusion for Incremental Learning in Semantic Segmentation

1 code implementation1 Feb 2022 Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Hao Tang, Xavier Alameda-Pineda, Elisa Ricci

To fill this gap, in this paper we introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting while accounting for semantic spatial- and channel-level dependencies.

Incremental Learning Semantic Segmentation

Modeling the Background for Incremental and Weakly-Supervised Semantic Segmentation

1 code implementation31 Jan 2022 Fabio Cermelli, Massimiliano Mancini, Samuel Rota Buló, Elisa Ricci, Barbara Caputo

To tackle these issues, we introduce a novel incremental class learning approach for semantic segmentation taking into account a peculiar aspect of this task: since each training step provides annotation only for a subset of all possible classes, pixels of the background class exhibit a semantic shift.

Segmentation Weakly supervised segmentation +2

Graph-based Generative Face Anonymisation with Pose Preservation

1 code implementation10 Dec 2021 Nicola Dall'Asen, Yiming Wang, Hao Tang, Luca Zanella, Elisa Ricci

With the goal to maintain the geometric attributes of the source face, i. e., the facial pose and expression, and to promote more natural face generation, we propose to exploit a Bipartite Graph to explicitly model the relations between the facial landmarks of the source identity and the ones of the condition identity through a deep model.

Face Detection Face Generation

Self-Supervised Models are Continual Learners

1 code implementation CVPR 2022 Enrico Fini, Victor G. Turrisi da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, Julien Mairal

Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale.

Continual Learning Representation Learning

Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation

1 code implementation19 Nov 2021 Guanglei Yang, Zhun Zhong, Hao Tang, Mingli Ding, Nicu Sebe, Elisa Ricci

Specifically, in the image translation stage, Bi-Mix leverages the knowledge of day-night image pairs to improve the quality of nighttime image relighting.

Autonomous Driving Image Relighting +3

Click to Move: Controlling Video Generation with Sparse Motion

1 code implementation ICCV 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

1 code implementation 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.

Clustering Contrastive Learning +1

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.

Knowledge Distillation Source-Free Domain Adaptation

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.

Blended-target Domain Adaptation Multi-target 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.

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

Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction

1 code implementation ICCV 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 Depth Prediction +1

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.

Graph Attention Monocular Depth Estimation +1

Viewing Graph Solvability via Cycle Consistency

1 code implementation ICCV 2021 Federica Arrigoni, Andrea Fusiello, Elisa Ricci, Tomas Pajdla

In structure-from-motion the viewing graph is a graph where vertices correspond to cameras and edges represent fundamental matrices.

Variational Structured Attention Networks for Dense Pixel-Wise Prediction

1 code implementation1 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 implementations ICCV 2021 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 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 Object +1

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 Object +2

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.

Clustering Deep Clustering +1

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 Keypoint Estimation +2

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.

Clustering Incremental Learning +1

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 Disjoint 10-1 +9

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 Object +2

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 Depth Prediction +2

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 Object Detection +2

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 Test

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.

Depth Prediction Knowledge Distillation +2

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.

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.

Object Recognition Online Domain Adaptation

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 Object Categorization +1

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 +1

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 Test

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.

Contour Detection

AutoDIAL: Automatic DomaIn Alignment Layers

2 code implementations 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 Test

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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