no code implementations • 20 Sep 2023 • Samuel Felipe dos Santos, Rodrigo Berriel, Thiago Oliveira-Santos, Nicu Sebe, Jurandy Almeida
Nevertheless, the models are usually larger than the baseline for a single domain.
no code implementations • 20 Sep 2023 • Samuel Felipe dos Santos, Nicu Sebe, Jurandy Almeida
In this paper, we propose a further study of the computational cost of deep models designed for the frequency domain, evaluating the cost of decoding and passing the images through the network.
no code implementations • 16 Sep 2023 • Lucas Fernando Alvarenga e Silva, Nicu Sebe, Jurandy Almeida
Convolutional Neural Networks (CNNs) have brought revolutionary advances to many research areas due to their capacity of learning from raw data.
1 code implementation • 19 May 2023 • Eduardo Nascimento, John Just, Jurandy Almeida, Tiago Almeida
In precision agriculture, detecting productive crop fields is an essential practice that allows the farmer to evaluate operating performance separately and compare different seed varieties, pesticides, and fertilizers.
no code implementations • 30 Nov 2022 • Mateus Roder, Jurandy Almeida, Gustavo H. de Rosa, Leandro A. Passos, André L. D. Rossi, João P. Papa
In the last decade, exponential data growth supplied machine learning-based algorithms' capacity and enabled their usage in daily-life activities.
no code implementations • 14 Oct 2022 • Samuel Felipe dos Santos, Rodrigo Berriel, Thiago Oliveira-Santos, Nicu Sebe, Jurandy Almeida
Nevertheless, the models are usually larger than the baseline for a single domain.
no code implementations • 28 Apr 2022 • Luiz H. Buris, Daniel C. G. Pedronette, Joao P. Papa, Jurandy Almeida, Gustavo Carneiro, Fabio A. Faria
Deep learning architectures have achieved promising results in different areas (e. g., medicine, agriculture, and security).
1 code implementation • 6 Sep 2021 • Lucas Fernando Alvarenga e Silva, Daniel Carlos Guimarães Pedronette, Fábio Augusto Faria, João Paulo Papa, Jurandy Almeida
Deep learning (DL) has been the primary approach used in various computer vision tasks due to its relevant results achieved on many tasks.
Multi-Source Unsupervised Domain Adaptation Unsupervised Domain Adaptation
no code implementations • 1 Apr 2021 • Samuel Felipe dos Santos, Jurandy Almeida
Most image data available are often stored in a compressed format, from which JPEG is the most widespread.
no code implementations • 26 Dec 2020 • Samuel Felipe dos Santos, Jurandy Almeida
For this reason, a preliminary decoding process is required, since video data are often stored in a compressed format.
no code implementations • 26 Dec 2020 • Samuel Felipe dos Santos, Nicu Sebe, Jurandy Almeida
In this paper, we propose a further study of the computational cost of deep models designed for the frequency domain, evaluating the cost of decoding and passing the images through the network.
no code implementations • 1 Jan 2020 • Jurandy Almeida, Cristiano Saltori, Paolo Rota, Nicu Sebe
Deep learning revolution happened thanks to the availability of a massive amount of labelled data which have contributed to the development of models with extraordinary inference capabilities.
1 code implementation • 1 Dec 2019 • Daniel Carlos Guimarães Pedronette, Lucas Pascotti Valem, Jurandy Almeida, and Ricardo da S. Torres
In this paper, a novel manifold ranking algorithm is proposed based on the hypergraphs for unsupervised multimedia retrieval tasks.
no code implementations • 18 Jul 2016 • Leonardo A. Duarte, Otávio A. B. Penatti, Jurandy Almeida
This model is used to map low-level frame vectors into high-level vectors (e. g., classifier probability scores).
no code implementations • 30 May 2015 • Leonardo A. Duarte, Otávio A. B. Penatti, Jurandy Almeida
The Bag of Genres video vector contains a summary of the activations of each genre in the video content.