Search Results for author: Jurandy Almeida

Found 15 papers, 3 papers with code

CNNs for JPEGs: A Study in Computational Cost

no code implementations20 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.

Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation

no code implementations16 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.

Data Augmentation Domain Adaptation

Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark Results

1 code implementation19 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.

Contrastive Learning

From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks

no code implementations30 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.

Action Recognition Domain Adaptation +1

Budget-Aware Pruning for Multi-Domain Learning

no code implementations14 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.

Mixup-based Deep Metric Learning Approaches for Incomplete Supervision

no code implementations28 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).

Memorization Metric Learning

Less is More: Accelerating Faster Neural Networks Straight from JPEG

no code implementations1 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.

CNNs for JPEGs: A Study in Computational Cost

no code implementations26 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.

Edge-computing Image Classification +1

Low-Budget Label Query through Domain Alignment Enforcement

no code implementations1 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.

Unsupervised Domain Adaptation

Bag of Attributes for Video Event Retrieval

no code implementations18 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).

Attribute Retrieval

Bag of Genres for Video Retrieval

no code implementations30 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.

Retrieval Video Retrieval

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