activity
20212024
most citedDoes CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?

41 citations · 54 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection

Weixing Wang, Haojin Yang, Christoph Meinel +3

In recent years, there has been a growing interest in using Machine Learning (ML), especially Deep Learning (DL) to solve Network Intrusion Detection (NID) problems. However, the f…

cs.CR202411 cited

Large Language Models in Cybersecurity: State-of-the-Art

Farzad Nourmohammadzadeh Motlagh, Mehrdad Hajizadeh, Mehryar Majd +3

The rise of Large Language Models (LLMs) has revolutionized our comprehension of intelligence bringing us closer to Artificial Intelligence. Since their introduction, researchers h…

cs.CL2023

Scaled Prompt-Tuning for Few-Shot Natural Language Generation

Ting Hu, Christoph Meinel, Haojin Yang

The increasingly Large Language Models (LLMs) demonstrate stronger language understanding and generation capabilities, while the memory demand and computation cost of fine-tuning L…

cs.LG20232 cited

Supervised Knowledge May Hurt Novel Class Discovery Performance

Ziyun Li, Jona Otholt, Ben Dai +3

Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset by leveraging prior knowledge of a labeled set comprising disjoint but related classes. Given tha…

cs.CV202141 cited

Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?

Sedigheh Eslami, Gerard de Melo, Christoph Meinel

Contrastive Language--Image Pre-training (CLIP) has shown remarkable success in learning with cross-modal supervision from extensive amounts of image--text pairs collected online.…