Search Results for author:
Found 22 papers, 9 papers with code
jurBERT: A Romanian BERT Model for Legal Judgement Prediction
Transformer-based models have become the de facto standard in the field of Natural Language Processing (NLP).
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors
We propose an efficient abnormal event detection model based on a lightweight masked auto-encoder (AE) applied at the video frame level.
Ranked #13 on
Anomaly Detection
on CUHK Avenue
VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web
The DarkWeb represents a hotbed for illicit activity, where users communicate on different market forums in order to exchange goods and services.
Self-paced learning to improve text row detection in historical documents with missing labels
An important preliminary step of optical character recognition systems is the detection of text rows.
Rethinking the Authorship Verification Experimental Setups
One of the main drivers of the recent advances in authorship verification is the PAN large-scale authorship dataset.
A realistic approach to generate masked faces applied on two novel masked face recognition data sets
We propose a method for enhancing data sets containing faces without masks by creating synthetic masks and overlaying them on faces in the original images.
Ranked #1 on
Face Recognition
on CASIA-WebFace+masks
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks
We propose $\textbf{EvoBA}$ as a query-efficient $L_0$ black-box adversarial attack which, together with the aforementioned methods, can serve as a generic tool to assess the empirical robustness of image classifiers.
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture.
Ranked #2 on
Anomaly Detection
on UCSD Peds2
Abnormal Event Detection In Video
Anomaly Detection In Surveillance Videos
+4
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
To generate useful data samples for training the student, our framework (i) learns to generate images on a proxy data set (with images and classes different from those used to train the black-box) and (ii) applies an evolutionary strategy to make sure that each generated data sample exhibits a high response for a specific class when given as input to the black box.
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
Following the standard formulation of abnormal event detection as outlier detection, we propose a background-agnostic framework that learns from training videos containing only normal events.
Abnormal Event Detection In Video
Anomaly Detection In Surveillance Videos
+2
A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI
In this work, we focus on explainable AI and propose a novel generic and model-agnostic framework for synthesizing input exemplars that maximize a desired response from a machine learning model.
Self-Supervised Representation Learning on Document Images
This work analyses the impact of self-supervised pre-training on document images in the context of document image classification.
Local Learning with Deep and Handcrafted Features for Facial Expression Recognition
We present an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve state-of-the-art results in facial expression recognition.
Ranked #4 on
Facial Expression Recognition (FER)
on FER2013
(using extra training data)
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Detecting abnormal events in video using Narrowed Normality Clusters
To detected abnormal events in the test video, we analyze each test sample and consider its maximum normality score provided by the trained one-class SVM models, based on the intuition that a test sample can belong to only one cluster of normality.
Ranked #15 on
Anomaly Detection
on CUHK Avenue
Can string kernels pass the test of time in Native Language Identification?
While most of our kernels are based on character p-grams (also known as n-grams) extracted from essays or speech transcripts, we also use a kernel based on i-vectors, a low-dimensional representation of audio recordings, provided by the shared task organizers.
Unmasking the abnormal events in video
We propose a novel framework for abnormal event detection in video that requires no training sequences.
Ranked #29 on
Anomaly Detection
on CUHK Avenue
Abnormal Event Detection In Video
Authorship Verification
+1
How hard can it be? Estimating the difficulty of visual search in an image
We address the problem of estimating image difficulty defined as the human response time for solving a visual search task.
UnibucKernel: An Approach for Arabic Dialect Identification Based on Multiple String Kernels
Our approach is shallow and simple, but the empirical results obtained in the ADI Shared Task prove that it achieves very good results.
Challenges in Representation Learning: A report on three machine learning contests
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge.
Ranked #12 on
Facial Expression Recognition (FER)
on FER2013
