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pro vyhledávání: '"Mullappilly, Sahal Shaji"'
Autor:
Mullappilly, Sahal Shaji, Gehlot, Abhishek Singh, Anwer, Rao Muhammad, Khan, Fahad Shahbaz, Cholakkal, Hisham
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence 2024
Conventional open-world object detection (OWOD) problem setting first distinguishes known and unknown classes and then later incrementally learns the unknown objects when introduced with labels in the subsequent tasks. However, the current OWOD formu
Externí odkaz:
http://arxiv.org/abs/2402.16013
Autor:
Pieri, Sara, Mullappilly, Sahal Shaji, Khan, Fahad Shahbaz, Anwer, Rao Muhammad, Khan, Salman, Baldwin, Timothy, Cholakkal, Hisham
In this paper, we introduce BiMediX, the first bilingual medical mixture of experts LLM designed for seamless interaction in both English and Arabic. Our model facilitates a wide range of medical interactions in English and Arabic, including multi-tu
Externí odkaz:
http://arxiv.org/abs/2402.13253
Autor:
Mullappilly, Sahal Shaji, Shaker, Abdelrahman, Thawakar, Omkar, Cholakkal, Hisham, Anwer, Rao Muhammad, Khan, Salman, Khan, Fahad Shahbaz
Publikováno v:
Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14126-14136
Climate change is one of the most significant challenges we face together as a society. Creating awareness and educating policy makers the wide-ranging impact of climate change is an essential step towards a sustainable future. Recently, Large Langua
Externí odkaz:
http://arxiv.org/abs/2312.09366
Autor:
Rasheed, Hanoona, Maaz, Muhammad, Mullappilly, Sahal Shaji, Shaker, Abdelrahman, Khan, Salman, Cholakkal, Hisham, Anwer, Rao M., Xing, Erix, Yang, Ming-Hsuan, Khan, Fahad S.
Large Multimodal Models (LMMs) extend Large Language Models to the vision domain. Initial LMMs used holistic images and text prompts to generate ungrounded textual responses. Recently, region-level LMMs have been used to generate visually grounded re
Externí odkaz:
http://arxiv.org/abs/2311.03356
Autor:
Thawkar, Omkar, Shaker, Abdelrahman, Mullappilly, Sahal Shaji, Cholakkal, Hisham, Anwer, Rao Muhammad, Khan, Salman, Laaksonen, Jorma, Khan, Fahad Shahbaz
The latest breakthroughs in large vision-language models, such as Bard and GPT-4, have showcased extraordinary abilities in performing a wide range of tasks. Such models are trained on massive datasets comprising billions of public image-text pairs w
Externí odkaz:
http://arxiv.org/abs/2306.07971
Self-supervised learning (SSL) methods such as masked language modeling have shown massive performance gains by pretraining transformer models for a variety of natural language processing tasks. The follow-up research adapted similar methods like mas
Externí odkaz:
http://arxiv.org/abs/2205.05543
Accuracy of English-language Question Answering (QA) systems has improved significantly in recent years with the advent of Transformer-based models (e.g., BERT). These models are pre-trained in a self-supervised fashion with a large English text corp
Externí odkaz:
http://arxiv.org/abs/2204.05814