Search Results for author:
Found 49 papers, 21 papers with code
$σ$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples
Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging.
Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training
Deep learning models undergo a significant increase in the number of parameters they possess, leading to the execution of a larger number of operations during inference.
Reassembling Broken Objects using Breaking Curves
Results show that our solution performs well in reassembling different kinds of broken objects.
Multi-Phase Relaxation Labeling for Square Jigsaw Puzzle Solving
We present a novel method for solving square jigsaw puzzles based on global optimization.
Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding
Knowledge Graphs (KGs) have proven to be a reliable way of structuring data.
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning
In this survey, we provide a comprehensive systematization of poisoning attacks and defenses in machine learning, reviewing more than 100 papers published in the field in the last 15 years.
Machine Learning Security against Data Poisoning: Are We There Yet?
The recent success of machine learning (ML) has been fueled by the increasing availability of computing power and large amounts of data in many different applications.
The Group Loss++: A deeper look into group loss for deep metric learning
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.
Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing Borders
This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders.
Energy-Latency Attacks via Sponge Poisoning
Sponge examples are test-time inputs carefully optimized to increase energy consumption and latency of neural networks when deployed on hardware accelerators.
Can machines learn to see without visual databases?
This paper sustains the position that the time has come for thinking of learning machines that conquer visual skills in a truly human-like context, where a few human-like object supervisions are given by vocal interactions and pointing aids only.
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions
Backdoor attacks inject poisoning samples during training, with the goal of forcing a machine learning model to output an attacker-chosen class when presented a specific trigger at test time.
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?
One of the most concerning threats for modern AI systems is data poisoning, where the attacker injects maliciously crafted training data to corrupt the system's behavior at test time.
Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI.
Transductive Visual Verb Sense Disambiguation
Verb Sense Disambiguation is a well-known task in NLP, the aim is to find the correct sense of a verb in a sentence.
DSLib: An open source library for the dominant set clustering method
This package provides an implementation of the original DS clustering algorithm since no code has been officially released yet, together with a still growing collection of methods and variants related to it.
Semantic Change Detection with Asymmetric Siamese Networks
Given two multi-temporal aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries.
A black-box adversarial attack for poisoning clustering
In an attempt to fill this gap, in this work, we propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms.
Efficient Tensor Kernel methods for sparse regression
Second, we use a Nystrom-type subsampling approach, which allows for a training phase with a smaller number of data points, so to reduce the computational cost.
The Group Loss for Deep Metric Learning
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.
Ranked #20 on
Metric Learning
on CUB-200-2011
(using extra training data)
Weakly Supervised Semantic Segmentation Using Constrained Dominant Sets
The availability of large-scale data sets is an essential pre-requisite for deep learning based semantic segmentation schemes.
Dominant Set Clustering and Pooling for Multi-View 3D Object Recognition
We improve upon these methods by introducing a view clustering and pooling layer based on dominant sets.
Unsupervised Domain Adaptation using Graph Transduction Games
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain.
Deep Constrained Dominant Sets for Person Re-identification
By optimizing the constrained clustering in an end-to-end manner, we naturally leverage the contextual knowledge of a set of images corresponding to the given person-images.
Ranked #2 on
Person Re-Identification
on CUHK03
(Rank-5 metric)
A Functional Representation for Graph Matching
In this paper, we present a functional representation for graph matching (FRGM) that aims to provide more geometric insights on the problem and reduce the space and time complexities of corresponding algorithms.
Is Data Clustering in Adversarial Settings Secure?
In this work we propose a general framework that allows one to identify potential attacks against clustering algorithms, and to evaluate their impact, by making specific assumptions on the adversary's goal, knowledge of the attacked system, and capabilities of manipulating the input data.
DeepScores and Deep Watershed Detection: current state and open issues
This paper gives an overview of our current Optical Music Recognition (OMR) research.
Characterization of Visual Object Representations in Rat Primary Visual Cortex
For most animal species, quick and reliable identification of visual objects is critical for survival.
Ancient Coin Classification Using Graph Transduction Games
Experiments are conducted on the only publicly available dataset which is composed of 180 images of 60 types of Roman coins.
Multi-feature Fusion for Image Retrieval Using Constrained Dominant Sets
In this paper, we propose a computationally efficient approach to fuse several hand-crafted and deep features, based on the probabilistic distribution of a given membership score of a constrained cluster in an unsupervised manner.
A Graph Transduction Game for Multi-target Tracking
Semi-supervised learning is a popular class of techniques to learn from labeled and unlabeled data.
Transductive Label Augmentation for Improved Deep Network Learning
Motivated by the observation that in virtually all application domains it is easy to obtain unlabeled data, in this paper we take a different perspective and propose a \emph{label augmentation} approach.
Speaker Clustering Using Dominant Sets
We report on a comprehensive set of experiments on the TIMIT dataset against standard clustering techniques and specific speaker clustering methods.
Sound Audio and Speech Processing
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects
We present the DeepScores dataset with the goal of advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding.
DOTA: A Large-scale Dataset for Object Detection in Aerial Images
The fully annotated DOTA images contains $188, 282$ instances, each of which is labeled by an arbitrary (8 d. o. f.)
Ranked #52 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Dominant Sets for "Constrained" Image Segmentation
In particular, we shall focus on interactive segmentation and co-segmentation (in both the unsupervised and the interactive versions).
Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets
In this paper, a unified three-layer hierarchical approach for solving tracking problems in multiple non-overlapping cameras is proposed.
On the Interplay between Strong Regularity and Graph Densification
In this paper we analyze the practical implications of Szemer\'edi's regularity lemma in the preservation of metric information contained in large graphs.
Large-scale Image Geo-Localization Using Dominant Sets
We cast the geo-localization as a clustering problem on local image features.
Revealing Structure in Large Graphs: Szemerédi's Regularity Lemma and its Use in Pattern Recognition
Introduced in the mid-1970's as an intermediate step in proving a long-standing conjecture on arithmetic progressions, Szemer\'edi's regularity lemma has emerged over time as a fundamental tool in different branches of graph theory, combinatorics and theoretical computer science.
Context Aware Nonnegative Matrix Factorization Clustering
These interactions allow the players to choose a cluster which is coherent with the clusters chosen by similar players, a property which is not guaranteed by NMF, since it produces a soft clustering of the data.
Randomized Prediction Games for Adversarial Machine Learning
In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time; e. g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits.
Interactive Image Segmentation Using Constrained Dominant Sets
We propose a new approach to interactive image segmentation based on some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs.
Document Clustering Games in Static and Dynamic Scenarios
Each document to be clustered is represented as a player and each cluster as a strategy.
A Game-Theoretic Approach to Word Sense Disambiguation
This paper presents a new model for word sense disambiguation formulated in terms of evolutionary game theory, where each word to be disambiguated is represented as a node on a graph whose edges represent word relations and senses are represented as classes.
Context-Sensitive Decision Forests for Object Detection
In this paper we introduce Context-Sensitive Decision Forests - A new perspective to exploit contextual information in the popular decision forest framework for the object detection problem.
A Game-Theoretic Approach to Hypergraph Clustering
In contrast to the classical approach, we attempt to provide a meaningful formalization of the very notion of a cluster and we show that game theory offers an attractive and unexplored perspective that serves well our purpose.
