9 citations · 21 across the 42 of their papers we have counts for
12 papers · 1 filter
LocalProp: Neuro-Localized Memory-Efficient Backpropagation
Diana-Nicoleta Grigore, Iuliana Georgescu, Radu Tudor Ionescu
The current deep learning training paradigm employs end-to-end backpropagation, regardless of the training stage, i.e. pre-training or fine-tuning. However, backpropagating through…
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…
Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts
Andrei-George Durdun, Victor Constantinescu, Radu Tudor Ionescu
Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and the interpretation of uttered…
Multilingual Coreference Resolution via Cycle-Consistent Machine Translation
Adriana-Valentina Costache, Eduard Poesina, Silviu-Florin Gheorghe +2
Coreference resolution is a core NLP task, having a broad range of downstream applications, e.g.~machine translation, question answering, document summarization, etc. While the tas…
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…
JumpLoRA: Sparse Adapters for Continual Learning in Large Language Models
Alexandra Dragomir, Ioana Pintilie, Antonio Barbalau +6
Adapter-based methods have become a cost-effective approach to continual learning (CL) for Large Language Models (LLMs), by sequentially learning a low-rank update matrix for each…
Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference
Kevin Wilkinghoff, Neelu Madan, Juan Miguel Valverde +6
Anomaly detection aims to identify observations that deviate from expected behavior. Because anomalous events are inherently sparse, most frameworks are trained exclusively on norm…
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…
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…
CLewR: Curriculum Learning with Restarts for Machine Translation Preference Learning
Alexandra Dragomir, Florin Brad, Radu Tudor Ionescu
Large language models (LLMs) have demonstrated competitive performance in zero-shot multilingual machine translation (MT). Some follow-up works further improved MT performance via…