Search Results for author: Jurandy Almeida

Found 11 papers, 2 papers with code

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

Parameter Sharing in Budget-Aware Adapters for Multi-Domain Learning

no code implementations14 Oct 2022 Samuel Felipe dos Santos, Rodrigo Berriel, Thiago Oliveira-Santos, Nicu Sebe, Jurandy Almeida

This work proposes a method capable of adapting to a user-defined budget while encouraging parameter sharing among domains.

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.

How Far Can We Get with Neural Networks Straight from JPEG?

no code implementations26 Dec 2020 Samuel Felipe dos Santos, Nicu Sebe, Jurandy Almeida

In this paper, we investigate the usage of CNNs that are designed to work directly with the DCT coefficients available in JPEG compressed images, proposing a handcrafted and data-driven techniques for reducing the computational complexity and the number of parameters for these models in order to keep their computational cost similar to their RGB baselines.

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).


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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