9 citations · 21 across the 42 of their papers we have counts for
22 papers · 1 filter
DF-MoE: Generalizable Deepfake Detection via Multimodal Sparse Mixture-of-Experts
Vlad Hondru, Florinel Alin Croitoru, Iuliana Georgescu +2
Audio-visual deepfake detection is an actively studied topic, where one of the main challenges is to develop detectors able to generalize across deepfake generation methods. We con…
MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectors
Bogdan Alexandru Bercean, Florinel Alin Croitoru, Vlad Hondru +3
Anomaly detection in medical images is a challenging task, since anomalies are not typically available during training. Recent methods leverage a single pretext task coupled with a…
NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report
Andrei Dumitriu, Aakash Ralhan, Florin Miron +32
This report presents the NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge, which targets automatic rip current understanding in images. Rip currents are haza…
SegMate: Asymmetric Attention-Based Lightweight Architecture for Efficient Multi-Organ Segmentation
Andrei-Alexandru Bunea, Dan-Matei Popovici, Radu Tudor Ionescu
State-of-the-art models for medical image segmentation achieve excellent accuracy but require substantial computational resources, limiting deployment in resource-constrained clini…
Curriculum-DPO++: Direct Preference Optimization via Data and Model Curricula for Text-to-Image Generation
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu +2
Direct Preference Optimization (DPO) has been proposed as an effective and efficient alternative to reinforcement learning from human feedback (RLHF). However, neither RLHF nor DPO…
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…
Curriculum Multi-Task Self-Supervision Improves Lightweight Architectures for Onboard Satellite Hyperspectral Image Segmentation
Hugo Carlesso, Josiane Mothe, Radu Tudor Ionescu
Hyperspectral imaging (HSI) captures detailed spectral signatures across hundreds of contiguous bands per pixel, being indispensable for remote sensing applications such as land-co…
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report
Andrei Dumitriu, Florin Miron, Florin Tatui +24
This report presents an overview of the AIM 2025 RipSeg Challenge, a competition designed to advance techniques for automatic rip current segmentation in still images. Rip currents…
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…
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…
MAVOS-DD: Multilingual Audio-Video Open-Set Deepfake Detection Benchmark
Florinel-Alin Croitoru, Vlad Hondru, Marius Popescu +3
We present the first large-scale open-set benchmark for multilingual audio-video deepfake detection. Our dataset comprises nearly 350 hours of real and fake videos across eight lan…
RipVIS: Rip Currents Video Instance Segmentation Benchmark for Beach Monitoring and Safety
Andrei Dumitriu, Florin Tatui, Florin Miron +3
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 identifi…
ExDDV: A New Dataset for Explainable Deepfake Detection in Video
Vlad Hondru, Eduard Hogea, Darian Onchis +1
The ever growing realism and quality of generated videos makes it increasingly harder for humans to spot deepfake content, who need to rely more and more on automatic deepfake dete…
DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-ID
Nyle Siddiqui, Florinel Alin Croitoru, Gaurav Kumar Nayak +2
With the recent exhibited strength of generative diffusion models, an open research question is if images generated by these models can be used to learn better visual representatio…
Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook
Florinel-Alin Croitoru, Andrei-Iulian Hiji, Vlad Hondru +7
We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content. We identify various kinds of deepfakes and co…
Multi-Level Feature Distillation of Joint Teachers Trained on Distinct Image Datasets
Adrian Iordache, Bogdan Alexe, Radu Tudor Ionescu
We propose a novel teacher-student framework to distill knowledge from multiple teachers trained on distinct datasets. Each teacher is first trained from scratch on its own dataset…
CBM: Curriculum by Masking
Andrei Jarca, Florinel-Alin Croitoru, Radu Tudor Ionescu
We propose Curriculum by Masking (CBM), a novel state-of-the-art curriculum learning strategy that effectively creates an easy-to-hard training schedule via patch (token) masking,…
PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction
Eduard Poesina, Adriana Valentina Costache, Adrian-Gabriel Chifu +2
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…
Curriculum Direct Preference Optimization for Diffusion and Consistency Models
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu +2
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 n…
Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers
Diana-Nicoleta Grigore, Mariana-Iuliana Georgescu, Jon Alvarez Justo +3
Few-shot knowledge distillation recently emerged as a viable approach to harness the knowledge of large-scale pre-trained models, using limited data and computational resources. In…
Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning
Jon Alvarez Justo, Alexandru Ghita, Daniel Kovac +5
Satellites are increasingly adopting on-board AI to optimize operations and increase autonomy through in-orbit inference. The use of Deep Learning (DL) models for segmentation in h…
Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation
Vlad Hondru, Radu Tudor Ionescu
Diffusion models showcase strong capabilities in image synthesis, being used in many computer vision tasks with great success. To this end, we propose to explore a new use case, na…