Zobrazeno 1 - 10
of 121
pro vyhledávání: '"Colombini, Esther"'
Visual Simultaneous Localization and Mapping (VSLAM) is a fundamental technology for robotics applications. While VSLAM research has achieved significant advancements, its robustness under challenging situations, such as poor lighting, dynamic enviro
Externí odkaz:
http://arxiv.org/abs/2410.12520
Data derived from the realm of the social sciences is often produced in digital text form, which motivates its use as a source for natural language processing methods. Researchers and practitioners have developed and relied on artificial intelligence
Externí odkaz:
http://arxiv.org/abs/2406.00393
Autor:
Santos, Gabriel Oliveira dos, Moreira, Diego A. B., Ferreira, Alef Iury, Silva, Jhessica, Pereira, Luiz, Bueno, Pedro, Sousa, Thiago, Maia, Helena, Da Silva, Nádia, Colombini, Esther, Pedrini, Helio, Avila, Sandra
This work introduces CAPIVARA, a cost-efficient framework designed to enhance the performance of multilingual CLIP models in low-resource languages. While CLIP has excelled in zero-shot vision-language tasks, the resource-intensive nature of model tr
Externí odkaz:
http://arxiv.org/abs/2310.13683
Autor:
Rossi, Leonardo de Lellis, Berto, Leticia Mara, Rohmer, Eric, Costa, Paula Paro, Gudwin, Ricardo Ribeiro, Colombini, Esther Luna, Simoes, Alexandre da Silva
The ability to automatically learn movements and behaviors of increasing complexity is a long-term goal in autonomous systems. Indeed, this is a very complex problem that involves understanding how knowledge is acquired and reused by humans as well a
Externí odkaz:
http://arxiv.org/abs/2305.00597
Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action's perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex,
Externí odkaz:
http://arxiv.org/abs/2302.09759
Autor:
Benatti, Raysa M., Villarroel, Camila M. L., Avila, Sandra, Colombini, Esther L., Severi, Fabiana C.
Natural language processing techniques have helped domain experts solve legal problems. Digital availability of court documents increases possibilities for researchers, who can access them as a source for building datasets -- whose disclosure is alig
Externí odkaz:
http://arxiv.org/abs/2211.00498
With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic increase in popularity, scaling to previously intractable problems, such as playing complex games from pixel observations, sustaining conversations
Externí odkaz:
http://arxiv.org/abs/2203.01387
Autor:
Santana, Alana, Colombini, Esther
Attention is a state of arousal capable of dealing with limited processing bottlenecks in human beings by focusing selectively on one piece of information while ignoring other perceptible information. For decades, concepts and functions of attention
Externí odkaz:
http://arxiv.org/abs/2112.05909
Publikováno v:
In Cognitive Systems Research December 2024 88
This paper shows that CIDEr-D, a traditional evaluation metric for image description, does not work properly on datasets where the number of words in the sentence is significantly greater than those in the MS COCO Captions dataset. We also show that
Externí odkaz:
http://arxiv.org/abs/2109.13701