Zobrazeno 1 - 10
of 2 290
pro vyhledávání: '"Mozafari, P."'
Autor:
Mozafari, Mohammad, Hasani, Hosein, Vahidimajd, Reza, Fereydooni, Mohamadreza, Baghshah, Mahdieh Soleymani
In recent years, few-shot segmentation (FSS) models have emerged as a promising approach in medical imaging analysis, offering remarkable adaptability to segment novel classes with limited annotated data. Existing approaches to few-shot segmentation
Externí odkaz:
http://arxiv.org/abs/2410.09967
Detecting and answering ambiguous questions has been a challenging task in open-domain question answering. Ambiguous questions have different answers depending on their interpretation and can take diverse forms. Temporally ambiguous questions are one
Externí odkaz:
http://arxiv.org/abs/2409.17046
Automatic Question Answering (QA) systems rely on contextual information to provide accurate answers. Commonly, contexts are prepared through either retrieval-based or generation-based methods. The former involves retrieving relevant documents from a
Externí odkaz:
http://arxiv.org/abs/2409.16096
Autor:
Abdollah, Ali, Izadi, Amirmohammad, Saghafian, Armin, Vahidimajd, Reza, Mozafari, Mohammad, Mirzaei, Amirreza, Samiei, Mohammadmahdi, Baghshah, Mahdieh Soleymani
Vision-language models (VLMs) like CLIP have showcased a remarkable ability to extract transferable features for downstream tasks. Nonetheless, the training process of these models is usually based on a coarse-grained contrastive loss between the glo
Externí odkaz:
http://arxiv.org/abs/2409.08206
Autor:
Tayaranian, Mohammadreza, Mozafari, Seyyed Hasan, Meyer, Brett H., Clark, James J., Gross, Warren J.
Transformer-based language models have shown state-of-the-art performance on a variety of natural language understanding tasks. To achieve this performance, these models are first pre-trained on general corpus and then fine-tuned on downstream tasks.
Externí odkaz:
http://arxiv.org/abs/2407.08887
The growing interest in brain-inspired computational models arises from the remarkable problem-solving efficiency of the human brain. Action recognition, a complex task in computational neuroscience, has received significant attention due to both its
Externí odkaz:
http://arxiv.org/abs/2406.11778
Digital education has gained popularity in the last decade, especially after the COVID-19 pandemic. With the improving capabilities of large language models to reason and communicate with users, envisioning intelligent tutoring systems (ITSs) that ca
Externí odkaz:
http://arxiv.org/abs/2404.04728
Publikováno v:
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024)
Nowadays, individuals tend to engage in dialogues with Large Language Models, seeking answers to their questions. In times when such answers are readily accessible to anyone, the stimulation and preservation of human's cognitive abilities, as well as
Externí odkaz:
http://arxiv.org/abs/2403.18426
Publikováno v:
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024)
Question answering (QA) and Machine Reading Comprehension (MRC) tasks have significantly advanced in recent years due to the rapid development of deep learning techniques and, more recently, large language models. At the same time, many benchmark dat
Externí odkaz:
http://arxiv.org/abs/2403.17859
Previous studies have shown that it is possible to map brain activation data of subjects viewing images onto the feature representation space of not only vision models (modality-specific decoding) but also language models (cross-modal decoding). In t
Externí odkaz:
http://arxiv.org/abs/2403.11771