activity
20242026
most citedDLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-ID

1 citations · 1 across the 6 of their papers we have counts for

collaborators

14 papers

cs.CL2026

MOSLD-Bench: Multilingual Open-Set Learning and Discovery Benchmark for Text Categorization

Adriana-Valentina Costache, Daria-Nicoleta Dragomir, Silviu-Florin Gheorghe +3

Open-set learning and discovery (OSLD) is a challenging machine learning task in which samples from new (unknown) classes can appear at test time. It can be seen as a generalizatio…

cs.CR2025

AutoMalDesc: Large-Scale Script Analysis for Cyber Threat Research

Alexandru-Mihai Apostu, Andrei Preda, Alexandra Daniela Damir +4

Generating thorough natural language explanations for threat detections remains an open problem in cybersecurity research, despite significant advances in automated malware detecti…

cs.CR2025

Every Character Counts: From Vulnerability to Defense in Phishing Detection

Maria Chiper, Radu Tudor Ionescu

Phishing attacks targeting both organizations and individuals are becoming an increasingly significant threat as technology advances. Current automatic detection methods often lack…

cs.CV2025

PRNU-Bench: A Novel Benchmark and Model for PRNU-Based Camera Identification

Florinel Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu

We propose a novel benchmark for camera identification via Photo Response Non-Uniformity (PRNU) estimation. The benchmark comprises 13K photos taken with 120+ cameras, where the tr…

cs.CV2025

SlotMatch: Distilling Object-Centric Representations for Unsupervised Video Segmentation

Diana-Nicoleta Grigore, Neelu Madan, Andreas Mogelmose +2

Unsupervised video segmentation is a challenging computer vision task, especially due to the lack of supervisory signals coupled with the complexity of visual scenes. To overcome t…

cs.CV2025

GeMix: Conditional GAN-Based Mixup for Improved Medical Image Augmentation

Hugo Carlesso, Maria Eliza Patulea, Moncef Garouani +2

Mixup has become a popular augmentation strategy for image classification, yet its naive pixel-wise interpolation often produces unrealistic images that can hinder learning, partic…