Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Leordeanu, Marius"'
Autor:
Masala, Mihai, Ilie-Ablachim, Denis C., Dima, Alexandru, Corlatescu, Dragos, Zavelca, Miruna, Olaru, Ovio, Terian, Simina, Terian, Andrei, Leordeanu, Marius, Velicu, Horia, Popescu, Marius, Dascalu, Mihai, Rebedea, Traian
In recent years, Large Language Models (LLMs) have achieved almost human-like performance on various tasks. While some LLMs have been trained on multilingual data, most of the training data is in English; hence, their performance in English greatly e
Externí odkaz:
http://arxiv.org/abs/2406.18266
Autor:
Masala, Mihai, Ilie-Ablachim, Denis C., Corlatescu, Dragos, Zavelca, Miruna, Leordeanu, Marius, Velicu, Horia, Popescu, Marius, Dascalu, Mihai, Rebedea, Traian
In recent years, Large Language Models (LLMs) have achieved almost human-like performance on various tasks. While some LLMs have been trained on multilingual data, most of the training data is in English. Hence, their performance in English greatly e
Externí odkaz:
http://arxiv.org/abs/2405.07703
There is an increasing number of real-world problems in computer vision and machine learning requiring to take into consideration multiple interpretation layers (modalities or views) of the world and learn how they relate to each other. For example,
Externí odkaz:
http://arxiv.org/abs/2402.08035
Face-to-face communication modeling in computer vision is an area of research focusing on developing algorithms that can recognize and analyze non-verbal cues and behaviors during face-to-face interactions. We propose an alternative to text chats for
Externí odkaz:
http://arxiv.org/abs/2402.06385
Artificial Intelligence makes great advances today and starts to bridge the gap between vision and language. However, we are still far from understanding, explaining and controlling explicitly the visual content from a linguistic perspective, because
Externí odkaz:
http://arxiv.org/abs/2309.08612
There are many ways of interpreting the world and they are highly interdependent. We exploit such complex dependencies and introduce a powerful multi-task hypergraph, in which every node is a task and different paths through the hypergraph reaching a
Externí odkaz:
http://arxiv.org/abs/2308.11021
Autor:
Marcu, Alina, Pirvu, Mihai, Costea, Dragos, Haller, Emanuela, Slusanschi, Emil, Belbachir, Ahmed Nabil, Sukthankar, Rahul, Leordeanu, Marius
We present a method for learning multiple scene representations given a small labeled set, by exploiting the relationships between such representations in the form of a multi-task hypergraph. We also show how we can use the hypergraph to improve a po
Externí odkaz:
http://arxiv.org/abs/2308.07615
We propose JEDI, a multi-dataset semi-supervised learning method, which efficiently combines knowledge from multiple experts, learned on different datasets, to train and improve the performance of individual, per dataset, student models. Our approach
Externí odkaz:
http://arxiv.org/abs/2308.04934
The task of generating novel views of real scenes is increasingly important nowadays when AI models become able to create realistic new worlds. In many practical applications, it is important for novel view synthesis methods to stay grounded in the p
Externí odkaz:
http://arxiv.org/abs/2306.14709