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Radu Tudor Ionescu

20 accepted papers

2026

AutoMalDesc: Large-Scale Script Analysis for Cyber Threat Research

Alexandru-Mihai Apostu, Andrei Preda, Alexandra Daniela Damir, Diana Bolocan, Radu Tudor Ionescu, Ioana Croitoru +1
AAAI 2026technical

Generating thorough natural language explanations for threat detections remains an open problem in cybersecurity research, despite significant advances in automated malware detection systems. In this work, we present AutoMalDesc, an automated static analysis summarization framework that, following i

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Curriculum Direct Preference Optimization for Diffusion and Consistency Models

Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Nicu Sebe, Mubarak Shah
CVPR 2025poster

Direct Preference Optimization (DPO) has been proposed as an effective and efficient alternative to reinforcement learning from human feedback (RLHF). In this paper, we propose a novel and enhanced version of DPO based on curriculum learning for text-to-image generation. Our method is divided into t…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2025

PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction

Eduard Poesina, Adriana Valentina Costache, Adrian-Gabriel Chifu, Josiane Mothe, Radu Tudor Ionescu
CVPR 2025poster

Text-to-image generation has recently emerged as a viable alternative to text-to-image retrieval, driven by the visually impressive results of generative diffusion models. Although query performance prediction is an active research topic in information retrieval, to the best of our knowledge, there…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2025

RipVIS: Rip Currents Video Instance Segmentation Benchmark for Beach Monitoring and Safety

Andrei Dumitriu, Florin Tatui, Florin Miron, Aakash Ralhan, Radu Tudor Ionescu, Radu Timofte
CVPR 2025poster

Rip currents are strong, localized and narrow currents of water that flow outwards into the sea, causing numerous beach-related injuries and fatalities worldwide. Accurate identification of rip currents remains challenging due to their amorphous nature and the lack of annotated data, which often req…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Task-Informed Anti-Curriculum by Masking Improves Downstream Performance on Text

Jarca Andrei, Florinel Alin Croitoru, Radu Tudor Ionescu
ACL 2025finding

Masked language modeling has become a widely adopted unsupervised technique to pre-train large language models (LLMs). However, the process of selecting tokens for masking is random, and the percentage of masked tokens is typically fixed for the entire training process. In this paper, we propose to…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors

Nicolae-C?t?lin Ristea, Florinel-Alin Croitoru, Radu Tudor Ionescu, Marius Popescu, Fahad Shahbaz Khan, Mubarak Shah
CVPR 2024poster

We propose an efficient abnormal event detection model based on a lightweight masked auto-encoder (AE) applied at the video frame level. The novelty of the proposed model is threefold. First we introduce an approach to weight tokens based on motion gradients thus shifting the focus from the static b…

Cited by 37Source ↗PDF ↗code ↗Scholar ↗
2023

A Novel Contrastive Learning Method for Clickbait Detection on RoCliCo: A Romanian Clickbait Corpus of News Articles

Daria Mihaela Broscoteanu, Radu Tudor Ionescu
EMNLP 2023short findings

To increase revenue, news websites often resort to using deceptive news titles, luring users into clicking on the title and reading the full news. Clickbait detection is the task that aims to automatically detect this form of false advertisement and avoid wasting the precious time of online users. D…

Cited by 0Source ↗code ↗Scholar ↗
2023

Audiovisual Masked Autoencoders

Mariana-Iuliana Georgescu, Eduardo Fonseca, Radu Tudor Ionescu, Mario Lucic, Cordelia Schmid, Anurag Arnab
ICCV 2023poster

Can we leverage the audiovisual information already present in video to improve self-supervised representation learning? To answer this question, we study various pretraining architectures and objectives within the masked autoencoding framework, motivated by the success of similar methods in natural…

Cited by 57PDF ↗code ↗Scholar ↗
2022

Rethinking the Authorship Verification Experimental Setups

Florin Brad, Andrei Manolache, Elena Burceanu, Antonio Barbalau, Radu Tudor Ionescu, Marius Popescu
EMNLP 2022main

One of the main drivers of the recent advances in authorship verification is the PAN large-scale authorship dataset. Despite generating significant progress in the field, inconsistent performance differences between the closed and open test sets have been reported. To this end, we improve the experi…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2022

Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection

Nicolae-Cătălin Ristea, Neelu Madan, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund +1
CVPR 2022oral

Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while being evaluated on both normal and abnormal test samples. Among the successful approaches for anomaly detection, a distinguished category of methods relies on…

Cited by 283PDF ↗code ↗Scholar ↗
2022

UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection

Andra Acsintoae, Andrei Florescu, Mariana-Iuliana Georgescu, Tudor Mare, Paul Sumedrea, Radu Tudor Ionescu +2
CVPR 2022poster

Detecting abnormal events in video is commonly framed as a one-class classification task, where training videos contain only normal events, while test videos encompass both normal and abnormal events. In this scenario, anomaly detection is an open-set problem. However, some studies assimilate anomal…

Cited by 177PDF ↗code ↗Scholar ↗
2022

VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web

Andrei Manolache, Florin Brad, Antonio Barbalau, Radu Tudor Ionescu, Marius Popescu
NeurIPS 2022accept

The Dark Web represents a hotbed for illicit activity, where users communicate on different market forums in order to exchange goods and services. Law enforcement agencies benefit from forensic tools that perform authorship analysis, in order to identify and profile users based on their textual cont…

Cited by 22Source ↗PDF ↗code ↗Scholar ↗
2021

A realistic approach to generate masked faces applied on two novel masked face recognition data sets

Tudor-Alexandru Mare, Georgian Duta, Iuliana Georgescu, Adrian Sandru, Bogdan Alexe, Marius Popescu +1
NeurIPS 2021poster

The COVID-19 pandemic raises the problem of adapting face recognition systems to the new reality, where people may wear surgical masks to cover their noses and mouths. Traditional data sets (e.g., CelebA, CASIA-WebFace) used for training these systems were released before the pandemic, so they now s…

Cited by 24Source ↗code ↗Scholar ↗
2021

Anomaly Detection in Video via Self-Supervised and Multi-Task Learning

Mariana-Iuliana Georgescu, Antonio Barbalau, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, Mubarak Shah
CVPR 2021poster

Anomaly detection in video is a challenging computer vision problem. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods without full supervision. In this paper, we approach anomalous event detection in video through self-supervised and mul…

Cited by 383PDF ↗Scholar ↗
2021

LiRo: Benchmark and leaderboard for Romanian language tasks

Stefan Daniel Dumitrescu, Petru Rebeja, Beata Lorincz, Mihaela Gaman, Andrei Avram, Mihai Ilie +14
NeurIPS 2021poster

Recent advances in NLP have been sustained by the availability of large amounts of data and standardized benchmarks, which are not available for many languages. As a small step towards addressing this we propose LiRo, a platform for benchmarking models on the Romanian language on nine standard tasks…

Cited by 32Source ↗code ↗Scholar ↗
2021

SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles

Ana-Cristina Rogoz, Gaman Mihaela, Radu Tudor Ionescu
ACL 2021short

In this work, we introduce a corpus for satire detection in Romanian news. We gathered 55,608 public news articles from multiple real and satirical news sources, composing one of the largest corpora for satire detection regardless of language and the only one for the Romanian language. We provide an…

Cited by 10Source ↗PDF ↗code ↗Scholar ↗
2020

Black-Box Ripper: Copying black-box models using generative evolutionary algorithms

Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu
NeurIPS 2020oral

We study the task of replicating the functionality of black-box neural models, for which we only know the output class probabilities provided for a set of input images. We assume back-propagation through the black-box model is not possible and its training images are not available, e.g. the model co…

Cited by 64Source ↗PDF ↗code ↗Scholar ↗
2019

Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video

Radu Tudor Ionescu, Fahad Shahbaz Khan, Mariana-Iuliana Georgescu, Ling Shao
CVPR 2019poster

Abnormal event detection in video is a challenging vision problem. Most existing approaches formulate abnormal event detection as an outlier detection task, due to the scarcity of anomalous data during training. Because of the lack of prior information regarding abnormal events, these methods are no…

Cited by 478PDF ↗Scholar ↗
2016

How Hard Can It Be? Estimating the Difficulty of Visual Search in an Image

Radu Tudor Ionescu, Bogdan Alexe, Marius Leordeanu, Marius Popescu, Dim P. Papadopoulos, Vittorio Ferrari
CVPR 2016poster

We address the problem of estimating image difficulty defined as the human response time for solving a visual search task. We collect human annotations of image difficulty for the PASCAL VOC 2012 data set through a crowd-sourcing platform. We then analyze what human interpretable image properties ca…

Cited by 164PDF ↗Scholar ↗