most citedMSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image Segmentation

2 citations · 2 across the 1 of their papers we have counts for

collaborators

5 papers

eess.IV20262 cited

MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image Segmentation

Rayan Merghani Ahmed, Adnan Iltaf, Mohamed Elmanna +5

Accurate segmentation of coronary Digital Subtraction Angiography (DSA) images is essential for diagnosing and treating coronary artery disease (CAD). Despite advances in deep lear…

cs.CL2026

Consistency-Aware Parameter-Preserving Knowledge Editing Framework for Multi-Hop Question Answering

Lingwen Deng, Yifei Han, Shijie Li +2

Parameter-Preserving Knowledge Editing (PPKE) enables updating models with new information without retraining or parameter adjustment. Recent PPKE approaches used knowledge graphs…

cs.CV2025

Context-Aware Pseudo-Label Scoring for Zero-Shot Video Summarization

Yuanli Wu, Long Zhang, Yue Du +1

We propose a rubric-guided, pseudo-labeled, and prompt-driven zero-shot video summarization framework that bridges large language models with structured semantic reasoning. A small…

cs.CL2025

TsqLoRA: Towards Sensitivity and Quality Low-Rank Adaptation for Efficient Fine-Tuning

Yu Chen, Yifei Han, Long Zhang +2

Fine-tuning large pre-trained models for downstream tasks has become a fundamental approach in natural language processing. Fully fine-tuning all model parameters is computationall…

cs.CV2025

Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge

Bin Li, Shenxi Liu, Yixuan Weng +3

Following the successful hosts of the 1-st (NLPCC 2023 Foshan) CMIVQA and the 2-rd (NLPCC 2024 Hangzhou) MMIVQA challenges, this year, a new task has been introduced to further adv…