Search Results for author: Andrea Cavallaro

Found 30 papers, 16 papers with code

Cross-Modal Knowledge Transfer via Inter-Modal Translation and Alignment for Affect Recognition

no code implementations2 Aug 2021 Vandana Rajan, Alessio Brutti, Andrea Cavallaro

For this reason, we aim to improve the performance of uni-modal affect recognition models by transferring knowledge from a better-performing (or stronger) modality to a weaker modality during training.

Sentiment Analysis Transfer Learning +1

Protecting gender and identity with disentangled speech representations

no code implementations22 Apr 2021 Dimitrios Stoidis, Andrea Cavallaro

Besides its linguistic content, our speech is rich in biometric information that can be inferred by classifiers.

Representation Learning Speaker Verification +1

An embedded multichannel sound acquisition system for drone audition

no code implementations17 Jan 2021 Michael Clayton, Lin Wang, Andrew McPherson, Andrea Cavallaro

Microphone array techniques can improve the acoustic sensing performance on drones, compared to the use of a single microphone.

Underwater image filtering: methods, datasets and evaluation

no code implementations22 Dec 2020 Chau Yi Li, Riccardo Mazzon, Andrea Cavallaro

The growing interest in underwater image filtering methods--including learning-based approaches used for both restoration and enhancement--and the associated challenges call for a comprehensive review of the state of the art.

Underwater Image Restoration

Robust Latent Representations via Cross-Modal Translation and Alignment

no code implementations3 Nov 2020 Vandana Rajan, Alessio Brutti, Andrea Cavallaro

The proposed multi-modal training framework uses cross-modal translation and correlation-based latent space alignment to improve the representations of the weaker modalities.

Emotion Recognition Translation

Benchmark for Anonymous Video Analytics

no code implementations30 Sep 2020 Ricardo Sanchez-Matilla, Andrea Cavallaro

Out-of-home audience measurement aims to count and characterize the people exposed to advertising content in the physical world.

Semantically Adversarial Learnable Filters

1 code implementation13 Aug 2020 Ali Shahin Shamsabadi, Changjae Oh, Andrea Cavallaro

The semantic adversarial loss considers groups of (semantic) labels to craft perturbations that prevent the filtered image being classified with a label in the same group.

DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data

2 code implementations5 Aug 2020 Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi

We introduce a dimension-adaptive pooling (DAP) layer that makes DNNs flexible and more robust to changes in sensor availability and in sampling rate.

Activity Recognition Imputation

Exploiting vulnerabilities of deep neural networks for privacy protection

1 code implementation19 Jul 2020 Ricardo Sanchez-Matilla, Chau Yi Li, Ali Shahin Shamsabadi, Riccardo Mazzon, Andrea Cavallaro

To address these limitations, we present an adversarial attack {that is} specifically designed to protect visual content against { unseen} classifiers and known defenses.

Adversarial Attack Quantization

Towards robust sensing for Autonomous Vehicles: An adversarial perspective

no code implementations14 Jul 2020 Apostolos Modas, Ricardo Sanchez-Matilla, Pascal Frossard, Andrea Cavallaro

Autonomous Vehicles rely on accurate and robust sensor observations for safety critical decision-making in a variety of conditions.

Autonomous Vehicles Decision Making

Novel-View Human Action Synthesis

1 code implementation6 Jul 2020 Mohamed Ilyes Lakhal, Davide Boscaini, Fabio Poiesi, Oswald Lanz, Andrea Cavallaro

We first estimate the 3D mesh of the target body and transfer the rough textures from the 2D images to the mesh.

Novel View Synthesis Video Generation

PrivEdge: From Local to Distributed Private Training and Prediction

1 code implementation12 Apr 2020 Ali Shahin Shamsabadi, Adria Gascon, Hamed Haddadi, Andrea Cavallaro

To address this problem, we propose PrivEdge, a technique for privacy-preserving MLaaS that safeguards the privacy of users who provide their data for training, as well as users who use the prediction service.

Image Compression

DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments

2 code implementations12 Apr 2020 Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, Andrea Cavallaro, Hamed Haddadi

We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neural Networks (DNNs).

Image Classification

Multi-view shape estimation of transparent containers

1 code implementation27 Nov 2019 Alessio Xompero, Ricardo Sanchez-Matilla, Apostolos Modas, Pascal Frossard, Andrea Cavallaro

The 3D localisation of an object and the estimation of its properties, such as shape and dimensions, are challenging under varying degrees of transparency and lighting conditions.

Semantic Segmentation

ColorFool: Semantic Adversarial Colorization

1 code implementation CVPR 2020 Ali Shahin Shamsabadi, Ricardo Sanchez-Matilla, Andrea Cavallaro

Instead, adversarial attacks that generate unrestricted perturbations are more robust to defenses, are generally more successful in black-box settings and are more transferable to unseen classifiers.

Adversarial Attack Colorization +1

Privacy and Utility Preserving Sensor-Data Transformations

1 code implementation14 Nov 2019 Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi

Sensitive inferences and user re-identification are major threats to privacy when raw sensor data from wearable or portable devices are shared with cloud-assisted applications.

Activity Recognition

EdgeFool: An Adversarial Image Enhancement Filter

1 code implementation27 Oct 2019 Ali Shahin Shamsabadi, Changjae Oh, Andrea Cavallaro

This loss function accounts for both image detail enhancement and class misleading objectives.

Denoising Image Enhancement

Learning Generalisable Omni-Scale Representations for Person Re-Identification

2 code implementations15 Oct 2019 Kaiyang Zhou, Yongxin Yang, Andrea Cavallaro, Tao Xiang

An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation.

Unsupervised Domain Adaptation Unsupervised Person Re-Identification

ConflictNET: End-to-End Learning for Speech-based Conflict Intensity Estimation

1 code implementation26 Sep 2019 Vandana Rajan, Alessio Brutti, Andrea Cavallaro

Computational paralinguistics aims to infer human emotions, personality traits and behavioural patterns from speech signals.

Towards Characterizing and Limiting Information Exposure in DNN Layers

no code implementations13 Jul 2019 Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Andrea Cavallaro, Hamed Haddadi

Pre-trained Deep Neural Network (DNN) models are increasingly used in smartphones and other user devices to enable prediction services, leading to potential disclosures of (sensitive) information from training data captured inside these models.

Omni-Scale Feature Learning for Person Re-Identification

5 code implementations ICCV 2019 Kaiyang Zhou, Yongxin Yang, Andrea Cavallaro, Tao Xiang

As an instance-level recognition problem, person re-identification (ReID) relies on discriminative features, which not only capture different spatial scales but also encapsulate an arbitrary combination of multiple scales.

Person Re-Identification

Concealing the identity of faces in oblique images with adaptive hopping Gaussian mixtures

no code implementations29 Oct 2018 Omair Sarwar, Bernhard Rinner, Andrea Cavallaro

To address this issue, we propose to pseudo-randomly modify the appearance of face regions in the images using a privacy filter that prevents a human or a face recogniser from inferring the identities of people.

Face Recognition

Mobile Sensor Data Anonymization

1 code implementation26 Oct 2018 Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi

Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions.

Activity Recognition

A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents

no code implementations17 Mar 2018 Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni

This paper focuses on multi-sensor anomaly detection for moving cognitive agents using both external and private first-person visual observations.

Anomaly Detection

Protecting Sensory Data against Sensitive Inferences

1 code implementation21 Feb 2018 Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, Hamed Haddadi

Results show that the proposed framework maintains the usefulness of the transformed data for activity recognition, with an average loss of only around three percentage points, while reducing the possibility of gender classification to around 50\%, the target random guess, from more than 90\% when using raw sensor data.

Activity Recognition

Distributed One-class Learning

no code implementations10 Feb 2018 Ali Shahin Shamsabadi, Hamed Haddadi, Andrea Cavallaro

A major advantage of the proposed filter over existing distributed learning approaches is that users cannot access, even indirectly, the parameters of other users.

One-class classifier

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